Time series data processing device, time series data processing method and program

Abstract

The time-series data processing device (1) includes an acquisition unit (11) that acquires metadata including text information on a dependent variable and attribute information on explanatory variable candidates, and time-series data on the dependent variable and the explanatory variable candidates, and a relevance determination unit (12) that determines a function that defines the relationship between the dependent variable and the explanatory variable candidates using the metadata and the time-series data.

Description

[Technical Field]

[0001] The present disclosure relates to a time series data processing device, a time series data processing method, and a program. [Background technology]

[0002] Efforts are being made to analyze the temporal fluctuation trends of data and utilize the analysis results in various fields. For example, Patent Document 1 describes an information processing device that selects candidate explanatory variables from data having an absolute value of a correlation coefficient equal to or greater than a predetermined value with respect to data corresponding to a target variable. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] International Application No. 2017 / 203601 Summary of the Invention [Problem to be solved by the invention]

[0004] In the conventional technology described in Patent Document 1, the relationship between a dependent variable and an explanatory variable is defined only by a correlation coefficient that indicates the statistical relationship between the time-series data of the dependent variable and the time-series data of the explanatory variable. Therefore, there is a problem in that an explanatory variable that has a semantic relationship with the dependent variable may not be selected as an explanatory variable to be used for predicting the dependent variable.

[0005] The present disclosure is intended to solve the above-mentioned problems, and aims to provide a time-series data processing device that can select explanatory variables that have a statistical correlation with time-series data of a dependent variable and a semantic relationship with the dependent variable as explanatory variables to be used in predicting the dependent variable. [Means for solving the problem]

[0006] The time-series data processing device according to the present disclosure includes an acquisition unit that acquires metadata including text information related to a dependent variable and attribute information consisting of one or more layers with different granularities related to explanatory variable candidates, and time-series data of the dependent variable and explanatory variable candidates; 、 Between the target variable and candidate explanatory variables A method for calculating the extraction relevance consisting of a combination of the statistical relevance of time-series data and the semantic relevance between words included in the text information of the target variable based on metadata and words included in the attribute information of the explanatory variable candidate. a degree of association determination unit that determines a function that defines a relationship; an extraction unit that extracts explanatory variables from the explanatory variable candidates based on a comparison between the extraction relevance calculated from the function determined by the relevance determination unit and a threshold value; Equipped with. [Effects of the Invention]

[0007] According to the present disclosure, a function defining the relationship between a dependent variable and candidate explanatory variables is determined using metadata and time-series data. The time-series data includes statistical associations between the dependent variable and the explanatory variables, and the metadata includes semantic associations between the dependent variable and the explanatory variables. As a result, the time-series data processing device according to the present disclosure can select explanatory variables that have a statistical correlation with the time-series data of the dependent variable and a semantic relationship with the dependent variable as explanatory variables to be used for predicting the dependent variable. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a block diagram showing an example of the configuration of a time-series data processing device according to a first embodiment. [Figure 2] 3 is a flowchart showing a time-series data processing method according to the first embodiment. [Figure 3] 3A and 3B are diagrams showing examples of keywords related to objective variables and attribute information related to explanatory variable candidates. [Figure 4] 4A, 4B, and 4C are diagrams showing examples of attribute information related to explanatory variable candidates, keywords related to objective variables, and semantic relevance levels. [Figure 5] 5A and 5B are block diagrams showing a hardware configuration for realizing the functions of the time-series data processing device according to the first embodiment. [Figure 6] FIG. 10 is a block diagram showing an example of the configuration of a time-series data processing device according to a second embodiment. [Figure 7] 10 is a flowchart showing a time-series data processing method according to the second embodiment. [Figure 8] 8A, 8B, 8C, and 8D are diagrams showing examples of attribute information, attribute pairs, hierarchical weights, and semantic similarities related to explanatory variable candidates. [Figure 9] 9A and 9B are diagrams showing a similarity matrix and distances between explanatory variables. [Figure 10] FIG. 11 is a block diagram showing an example of the configuration of a time-series data processing device according to a third embodiment. [Figure 11] FIG. 11 is a screen diagram showing a screen example (1) in the third embodiment. [Figure 12] FIG. 11 is a screen diagram showing a screen example (2) in the third embodiment. [Figure 13] FIG. 13 is a screen diagram showing a screen example (3) in the third embodiment. [Figure 14] FIG. 10 is a block diagram showing an example of the configuration of a time-series data processing device according to a fourth embodiment. [Figure 15] 11 is a flowchart showing the operation of a testing unit in the fourth embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0009] Embodiment 1 The time-series data processing device according to the first embodiment selects, from among the explanatory variable candidates, explanatory variables that are statistically and semantically related to the objective variable.

[0010] (Basic configuration of time series data processing device) FIG. 1 is a block diagram showing an example of the configuration of a time series data processing device 1 according to a first embodiment. The time series data processing device 1 selects explanatory variables to be used for predicting a target variable from among a plurality of types of explanatory variables. The explanatory variables are time series data and are parameters detected in a time series. For example, the explanatory variables are parameters that change over time. The way in which the explanatory variables change over time may be arbitrary.

[0011] The dependent variable is a parameter that is affected by multiple types of explanatory variables, and its value changes when the values ​​of the explanatory variables change. The dependent variable is also time-series data whose value changes depending on the explanatory variables and over time. For example, if the dependent variable is the demand for air conditioners, the explanatory variables could be data on sales of air conditioners over a certain period of time.

[0012] 1, the time series data processing device 1 includes an acquisition unit 11, a relevance determination unit 12, and an extraction unit 13. For example, the time series data processing device 1 is realized by a computer. A memory included in the computer stores a program constituting an information processing application for realizing the functions of the acquisition unit 11, the relevance determination unit 12, and the extraction unit 13. A processor included in the computer executes the information processing application read from the memory, thereby realizing the functions of the acquisition unit 11, the relevance determination unit 12, and the extraction unit 13.

[0013] (Acquisition Department) The acquisition unit 11 acquires time-series data and metadata. For example, it is assumed that the time-series data processing device 1 includes a communication unit (not shown in FIG. 1) and that this communication unit enables communication with an external device (not shown in FIG. 1). In this case, the acquisition unit 11 controls the communication unit to acquire the time-series data and metadata from the external device. The external device is, for example, a data server. Examples of communication methods used for communication with the external device include Bluetooth (registered trademark), Wi-Fi, Zigbee (registered trademark), LoRa (registered trademark), and NFC (Near Field Communication). The communication unit is, for example, a wireless communication module compatible with any of these communication methods.

[0014] The acquisition unit 11 also controls the communication unit to execute procedures such as pairing, network connection, and address assignment to establish communication with the external device. Once communication is established, the communication unit requests time-series data and metadata from the external device, and the external device returns the requested data. The data received by the communication unit is acquired by the acquisition unit 11.

[0015] The time series data includes time series data on the objective variable and time series data on candidate explanatory variables. Identification information for identifying the data is attached to the time series data. For example, as identification information, the time series data related to the objective variable is given the data name "demand for air conditioner A," and the time series data related to the candidate explanatory variables is given the data name "data related to sales of air conditioner A in period B." Hereinafter, the names of the time series data of multiple explanatory variable candidates will be referred to as "explanatory variable a, explanatory variable b, explanatory variable c, ...". Also, the sampling periods of the time series data of the objective variable and the explanatory variable candidates are all assumed to be the same. For example, all data may be monthly data or all data may be daily data.

[0016] The metadata is data that includes keywords related to the objective variable and attribute information related to the explanatory variable candidates. Keywords are text information related to the objective variable and are specified by the user. For example, if the objective variable is "demand volume of a certain product," keywords are determined based on information such as the product's use, destination, related parts, and leading indicators. More specifically, when the objective variable is "demand volume of air conditioner products," there are keywords that indicate the air conditioner products themselves, such as "air conditioner," "air conditioner," and "outdoor unit."

[0017] The attribute information on the explanatory variable candidates is attribute or category information that indicates the classification of data assigned to each explanatory variable candidate. The attribute information may be structured in multiple layers. For example, if the objective variable is "demand for air conditioner products," a keyword related to this objective variable is "air conditioner." The attribute information on the explanatory variable candidates that affect this objective variable includes the major classification information "machinery" because "air conditioner" is a machine, and the medium classification information "inventory" related to demand.

[0018] (Relevance determination unit) The relevance determination unit 12 determines a function that defines the relationship between the objective variable and the explanatory variable candidates using the metadata and time-series data. For example, the relevance determination unit 12 determines a function type according to the objective variable and the explanatory variable candidates from among various function types that calculate numerical information about the relationship between the objective variable and the explanatory variable candidates using the metadata and time-series data. The function type may be any type that calculates numerical information when metadata and time-series data are substituted.

[0019] Determining the function also includes selecting a trained model generated by machine learning. For example, when metadata and time-series data are input, the relevance determining unit 12 selects a trained model that outputs numerical information indicating the relationship between the objective variable and the explanatory variable candidates. The trained model may be constructed using algorithms such as Transformer, BERT (Bidirectional Encoder Representations from Transformers), GPT (Generative Pre-Training), etc., or may be constructed by combining multiple algorithms including these.

[0020] (Extraction part) The extraction unit 13 extracts explanatory variables from the explanatory variable candidates based on a comparison between the function value determined by the relevance determination unit 12 and a threshold. For example, the extraction unit 13 extracts explanatory variable candidates corresponding to function values ​​exceeding a threshold from among a plurality of explanatory variable candidates. This enables the time series data processing device 1 to extract explanatory variables effective for predicting the objective variable. The threshold value is a threshold value related to the value of a function that is preset for the extraction unit 13, and is determined based on, for example, a prediction result of a response variable using the extracted explanatory variables.

[0021] In FIG. 1, the extraction unit 13 is provided in the time series data processing device 1, but it may be provided in an external device separate from the time series data processing device 1. For example, the relevance determination unit 12 connects to an external device via a communication unit included in the time series data processing device 1 and transmits information indicating the determined function to the external device. An extraction unit included in the external device extracts explanatory variables from the explanatory variable candidates based on a comparison between the value of the function received from the time series data processing device 1 and a threshold. For example, the external device may be a prediction device that predicts a dependent variable based on the extracted explanatory variables.

[0022] Next, a time-series data processing method according to the first embodiment will be described. FIG. 2 is a flowchart showing the time series data processing method according to the first embodiment, and shows a series of processes performed by the time series data processing device 1. The acquiring unit 11 acquires time-series data on the dependent variable, time-series data on the explanatory variable candidates, and metadata including keywords on the dependent variable and attribute information on the explanatory variable candidates (step ST1). For example, the time-series data on the dependent variable and the time-series data on the explanatory variable candidates are data sets acquired at certain intervals.

[0023] The dependent variable is data that is the target of prediction or analysis. When the dependent variable is "demand for air conditioner products," the time-series data related to the dependent variable is, for example, time-series data on the demand for air conditioner products over a certain period of time. The explanatory variable is data that can affect the dependent variable. When the dependent variable is "demand for air conditioner products," candidate explanatory variables that affect the demand include economic statistics data, meteorological data, and product data.

[0024] When the candidate explanatory variable is "economic statistical data," the time series data may be data on orders, production, shipments, sales, and inventory related to electronic parts, electrical equipment, machinery, etc., or data on construction statistics, demographic statistics, labor markets, foreign trade, banking statistics, energy, etc. When the candidate explanatory variable is "weather data," the time series data includes temperature (daily maximum temperature, minimum temperature, average temperature, etc.), humidity (daily average humidity, maximum humidity, minimum humidity, etc.), precipitation (daily precipitation amount, etc.), wind speed (average wind speed, maximum wind speed), etc. When the explanatory variable candidate is "product data," the time series data includes inventory levels (current inventory quantity), price fluctuations (fluctuations in product prices), and the like.

[0025] The metadata includes keywords as meta-information related to the objective variable and attribute information as meta-information related to the candidate explanatory variables. For example, it is assumed that metadata related to the target of prediction or analysis is generated in advance and stored in a data server. FIG. 3A is a diagram showing keywords related to the objective variable. In FIG. 3A, the objective variable is the demand for air conditioner products. In this case, as shown in FIG. 3A, the keywords related to the objective variable include "air conditioner," "air conditioner," and "outdoor unit," which are related to air conditioner products. In addition, based on "start of construction," which can be a leading indicator of the flow of demand generation for air conditioner products, keywords such as "construction," "start of construction," "building," and "housing" are listed.

[0026] FIG. 3B illustrates attribute information related to candidate explanatory variables. For example, in FIG. 3B, explanatory variables a, b, c, . . . , h, i, and i are candidate explanatory variables. Explanatory variables a, b, and c correspond to the objective variable whose keywords are "construction" and "construction start." Explanatory variables a and b contain information related to "construction" and "construction start." The attribute information for explanatory variables a and b indicates that the major category is "construction" and the intermediate category is "construction start." Explanatory variable c contains information related to "construction" and "orders." The attribute information for explanatory variable c indicates that the major category is "construction" and the intermediate category is "orders." While FIG. 3B illustrates a case in which explanatory variables correspond to keywords, the two do not necessarily need to correspond to each other. For example, the time series data processing device 1 can determine whether to remove explanatory variables unrelated to the objective variable that do not correspond to keywords by thresholding their relevance.

[0027] The explanatory variable h and explanatory variable i correspond to the objective variables whose keywords are "air conditioner," "air conditioner," and "outdoor unit." The explanatory variable h includes information belonging to "machinery" corresponding to "air conditioner," "air conditioner," or "outdoor unit," etc., and includes information about its "production." As shown in Figure 3B, the attribute information of the explanatory variable h includes the major category of "machinery" and the medium category of "production." Similarly, the explanatory variable i includes information belonging to "machinery" corresponding to "air conditioner," "air conditioner," or "outdoor unit," etc., and includes information about its "inventory." The attribute information of the explanatory variable i includes the major category of "machinery" and the medium category of "inventory."

[0028] As a process of determining the degree of association, the relevance determining unit 12 determines a function that defines the relationship between the objective variable and the explanatory variable candidates using the metadata and the time-series data (step ST2). For example, the relevance determination unit 12 determines a function for calculating a relevance for extraction, which is a combination of the statistical relevance of the time-series data between the dependent variable and the explanatory variable candidate, and the semantic relevance between the dependent variable and the explanatory variable candidate based on the metadata. This enables the time-series data processing device 1 to select explanatory variables that are semantically related to the dependent variable, in addition to the statistical relevance with the time-series data of the dependent variable.

[0029] The statistical relevance is an index that indicates the statistical closeness between the time series data for the dependent variable and the time series data for the candidate explanatory variables. This relevance is the degree of relevance that takes into account the data trend, small fluctuations, or phase shift between the waveforms formed by the time series data for the dependent variable and the waveforms formed by the time series data for the candidate explanatory variables.

[0030] Semantic relevance is an index showing the semantic closeness between keywords related to the objective variable and attribute information related to the explanatory variable candidates. The attribute information related to the explanatory variable candidates is related to the specific field or area to which the explanatory variable candidates belong, and can also be considered domain knowledge. Therefore, semantic relevance is also an index of the domain knowledge relevance between keywords related to the objective variable and attribute information related to the explanatory variable candidates.

[0031] (Pretreatment) The relevance determination unit 12 performs missing value processing, standardization processing, and data conversion processing on the time-series data of the dependent variable and the explanatory variable candidates, and calculates the value of the function using the processed time-series data. For example, the relevance determination unit 12 performs the above processing on the time-series data related to the dependent variable and the time-series data related to the explanatory variable candidates acquired by the acquisition unit 11. This makes it possible to more accurately calculate the relationship between the dependent variable and the explanatory variable candidates using the function determined by the relevance determination unit 12.

[0032] Missing value handling is a process of interpolating missing values ​​in time series data, and includes, for example, processes that interpolate using previous and subsequent values ​​in time (carry forward method, backward fill method), linear interpolation, and time-weighted interpolation. The carry forward method interpolates missing values ​​using previous values ​​in time, and the backward fill method interpolates missing values ​​using later values ​​in time. Linear interpolation is a process that linearly interpolates missing values ​​using previous and subsequent data in time. Time-weighted interpolation is a process that interpolates using the average or median value of the data over a specific period of time.

[0033] Standardization is a process of standardizing the scale of data to make it easier to compare different features, and examples of this include standardization and normalization. Standardization is a process of setting the mean of the data to 0 and the standard deviation to 1. Normalization is a process of scaling the data to a range from 0 to 1.

[0034] Data transformation processes change the properties of data to make it easier to analyze or model, and include, for example, moving averages, difference series, logarithmic transformations, seasonal adjustments, or combinations of these. Note that moving averages are processes that divide the sum of data in a specified range by the number of data points in order to smooth the data. The differential series is a process of taking the difference from the previous value in time to remove trends. The logarithmic transformation is a process of converting data into a logarithm when the data distribution is close to a log-normal distribution. The seasonal adjustment is a process of adjusting data at a specific cycle to remove seasonality. By performing these processes or a combination of these processes on time series data, the relevance determination unit 12 can calculate more accurate relevance for extraction.

[0035] The relevance determination unit 12 calculates the extraction relevance as a function value for the determined function using the time-series data after the above processing. The extraction relevance calculated by the relevance determination unit 12 is output to the extraction unit 13.

[0036] 2, the extraction unit 13 extracts explanatory variables from the explanatory variable candidates based on a comparison between the extraction relevance and a threshold (step ST3). For example, the extraction unit 13 extracts explanatory variables corresponding to the extraction relevance that exceeds the threshold from the multiple explanatory variable candidates. Hereinafter, the explanatory variables extracted by the extraction unit 13 will be referred to as first extracted explanatory variable data. For example, the first extracted explanatory variable data extracted by the time-series data processing device 1 is output to a prediction device. The prediction device predicts a dependent variable using the first extracted explanatory variable data. By executing the processes from step ST1 to step ST3, the time-series data processing device 1 can select explanatory variables that have a statistical correlation with the time-series data of the dependent variable and a semantic relationship with the dependent variable as first extracted explanatory variable data to be used for predicting the dependent variable.

[0037] (Example of relevance calculation) The function for calculating the extraction relevance degree is a function having a first part and a second part. The first part is a part for calculating the correlation coefficient, dynamic time warping (hereinafter referred to as DTW), Euclidean distance, or a combination of these values ​​for the time series data between the dependent variable and the candidate explanatory variables as the statistical relevance of the time series data between the dependent variable and the candidate explanatory variables. The second part is a part for calculating the cosine similarity between the vectorized text information of the objective variable and the attribute information of the explanatory variable candidate as the semantic relevance between the text information of the objective variable and the attribute information of the explanatory variable candidate.

[0038] (Calculation of statistical relevance) In the first part, the statistical relevance of the time series data is calculated for each candidate explanatory variable. For example, the first part is composed of a combination of three indices for calculating statistical relevance: correlation coefficient, DTW, and Euclidean distance. In the first part, the score obtained for each indices is converted to a value between 0 (low) and 1 (high), and the statistical relevance is calculated by taking the weighted average of these.

[0039] The time series data (x, y) of the objective variable and the explanatory variable candidates input to the first part is normalized by the relevance determining unit 12 so that the value of each point falls within the range of 0 to 1 according to the following formula (1). x'=(x-min(x)) / (max(x)-min(x)) y'=(y-min(y)) / (max(y)-min(y)) (1)

[0040] The correlation coefficient, which is an index for calculating the degree of statistical association, is a statistical quantity that indicates the strength of the linear relationship between time series data related to the objective variable and time series data related to the candidate explanatory variables. The value of the correlation coefficient ranges from -1 to 1, with values ​​closer to 1 indicating a stronger positive correlation and values ​​closer to -1 indicating a stronger negative correlation. The relevance determination unit 12 calculates a correlation coefficient s for the pair x', y' of normalized data in accordance with the following formula (2): cor Calculate. s cor =|cor(x',y')|∈[0,1] (2)

[0041] DTW is an index for evaluating the similarity of time series data, and is effective even when the time axes are misaligned. DTW optimally aligns two time series data to calculate the distance. The relevance determination unit 12 calculates a DTW score s for a pair of normalized data x', y', taking into account positive and negative values, according to the following formula (3): dtw In the following formula (3), N is the time series data length, and dtw(x', y') is the DTW distance. dtw p is the score of dtw(x',y'), which is the normal DTW distance. dtw n is the score when the data is inverted. dtw is the final score, a value between 0 and 1. s dtw p =1-(dtw(x',y')) / N s dtw n=1-(dtw(1-x',y')) / N s dtw =max(s dtw p ,s dtw n )∈[0,1] (3)

[0042] Euclidean distance is the straight-line distance between two data points, and in time series data, it is calculated by comparing values ​​at the same time point. The relevance determination unit 12 calculates the score s of the Euclidean distance for the pair x', y' of the normalized data, taking into account positive and negative values, according to the following formula (4): L2 In the following formula (4), N is the length of the time series data. 2 (x',y') is the Euclidean distance. L2 p is the normal Euclidean distance L 2 The score of (x',y'). L2 n is the score when the data is inverted. L2 is the final score, a value between 0 and 1. s L2 p =1-(L 2 (x',y') / √N) s L2 n =1-(L 2 (1-x',y') / √N) s L2 =max(s L2 p ,s L2 n )∈[0,1] (4)

[0043] The relevance determination unit 12 calculates the weighted average value s of the above three indices as the statistical relevance in accordance with the following formula (5): In the following formula (5), i is a subscript indicating the three indices, namely, the correlation coefficient, the DTW, and the Euclidean distance. i is the score of the index indicated by the subscript i, and the correlation coefficient score s cor , DTW score s dtw, and the Euclidean distance score s L2 It is. i is the weight given to the score of each indicator. s=Σ i∈{dtw,cor,L2} w i s i ∈[0,1] Σ i∈{dtw,cor,L2} w i =1 (5)

[0044] (Calculation of semantic relevance) In the second part, a semantic relevance is calculated by quantifying the degree of match between a keyword related to the objective variable designated by the user and attribute information related to the explanatory variable candidates.

[0045] For example, in the second part, the target variable keywords and attribute information of the explanatory variable candidates are vectorized, and the cosine similarity between the keyword vectors and the attribute information vectors is calculated as the semantic relevance. Vectorization (numerical representation) is achieved using word2vec or BERT, etc. Word2vec is a process that maps words to fixed-length vectors. BERT converts tokens from text information, and the BERT model outputs vector information when tokens are input. These vectors can capture the semantic relationships between words. However, if the cosine similarity is a negative value, the vector is set to 0.

[0046] FIG. 4A is a diagram showing an example of attribute information related to explanatory variable candidates. In FIG. 4A, explanatory variable a, explanatory variable b, explanatory variable c,..., explanatory variable h, explanatory variable i,... are explanatory variable candidates. Explanatory variables a, b, and c correspond to objective variables whose keywords are "construction" and "start of construction." Explanatory variables a and b contain information belonging to "construction" and "start of construction," etc. In the attribute information of explanatory variables a and b, the major category is "construction" and the medium category is "start of construction." Explanatory variable c contains information belonging to "construction" and "orders," etc. In the attribute information of explanatory variable c, the major category is "construction" and the medium category is "orders."

[0047] The explanatory variable h and explanatory variable i correspond to the objective variables whose keywords are "air conditioner," "air conditioner," and "outdoor unit." The explanatory variable h includes information belonging to "machinery" corresponding to "air conditioner," "air conditioner," or "outdoor unit," etc., and includes information about its "production." As shown in FIG. 4A, the attribute information of the explanatory variable h includes the major category of "machinery" and the medium category of "production." Similarly, the explanatory variable i includes information belonging to "machinery" corresponding to "air conditioner," "air conditioner," or "outdoor unit," etc., and includes information about its "inventory." The attribute information of the explanatory variable i includes the major category of "machinery" and the medium category of "inventory."

[0048] Fig. 4B is a diagram showing examples of keywords related to the objective variable. In Fig. 4B, the objective variable is the demand for air conditioner products. In this case, the keywords related to the objective variable include "air conditioner," "air conditioner," and "outdoor unit," which are related to air conditioner products, as shown in Fig. 4B. In addition, based on the fact that "construction starts" can be a leading indicator of the flow of demand for air conditioner products, the keywords listed are "construction," "construction starts," "buildings," and "housing." In the example shown in Figure 4B, keywords related to the objective variable are weighted according to their specificity. Alternatively, weights may be assigned in descending order of priority. The specific keywords "air conditioner" and "construction" are assigned a weight of "1.00," while the more abstract keyword "construction commenced" is assigned a weight of "0.95."

[0049] Fig. 4C is a diagram showing an example of semantic relevance (semantic relevance). In Fig. 4C, explanatory variable a, explanatory variable b, explanatory variable c,..., explanatory variable h, explanatory variable i,... are the explanatory variable candidates shown in Fig. 4A. As shown in Fig. 4B, when there are multiple keywords for a certain objective variable, the relevance determination unit 12 calculates the semantic relevance for each keyword. For example, the explanatory variable candidates corresponding to the keyword "architecture" related to the objective variable are explanatory variables a, b, and c, whose attribute information is classified as "architecture." Also, the explanatory variable candidates corresponding to the keyword "air conditioner" related to the objective variable are explanatory variables h and i, whose attribute information is classified as "machine."

[0050] When there are multiple keywords, the relevance determination unit 12 calculates the semantic relevance for each of these keywords (process (1)). For example, when there are five keywords, the relevance determination unit 12 calculates five semantic relevances corresponding to the respective keywords for the explanatory variable a.

[0051] If a weight for specificity or priority is assigned to a keyword, the relevance determination unit 12 multiplies the semantic relevance by the weight (process (2)). For example, as shown in FIG. 4B, if a weight of "0.95" is assigned to the keyword "construction commenced," the semantic relevance corresponding to the keyword "construction commenced" for the explanatory variable a is multiplied by the weight of "0.95." As a result, the semantic relevance corresponding to a keyword with high specificity or high priority becomes larger than the semantic relevance of a keyword with low specificity or low priority. Note that if a weight for specificity or priority is not assigned, process (2) of multiplying the semantic relevance by the weight is omitted.

[0052] Then, the relevance determination unit 12 adopts the largest value of the calculated semantic relevance (process (3)). For example, if there are five keywords, the largest value of the five semantic relevances is adopted.

[0053] When the attribute information regarding the explanatory variable candidates is hierarchical information, the semantic relevance is calculated for each layer. For example, when there are multiple layers such as the "major categories" and "medium categories" shown in FIG. 4A, the relevance determination unit 12 performs processes (1), (2), and (3) for each layer to calculate the semantic relevance for each layer. The relevance determination unit 12 calculates a weighted average of the semantic relevance for each layer and obtains one semantic relevance for each explanatory variable. For example, if there are two hierarchical levels, two semantic relevances are calculated for each explanatory variable, and a weighted average of the two semantic relevances is calculated. Note that the weights may be specified by the user. For example, the weight may be increased in descending order of priority set for the keyword or in descending order of specificity.

[0054] For example, in the function determined by the relevance determination unit 12, a weighted average of the statistical relevance calculated by the first part and the semantic relevance calculated by the second part is calculated as the relevance for extraction. This enables the time-series data processing device 1 to select explanatory variables that have a statistical relevance with the time-series data of the dependent variable and a semantic relationship with the dependent variable as the first extracted explanatory variable data to be used for predicting the dependent variable.

[0055] Next, a hardware configuration for realizing the functions of the time-series data processing device 1 will be described. The functions of the acquisition unit 11, the relevance determination unit 12, and the extraction unit 13 included in the time series data processing device 1 are realized by a processing circuit. That is, the time series data processing device 1 includes a processing circuit for executing the processes from step ST1 to step ST3 shown in Fig. 2. The processing circuit may be dedicated hardware, or may be a CPU (Central Processing Unit) that executes a program stored in a memory.

[0056] FIG. 5A is a block diagram showing a hardware configuration for realizing the functions of the time series data processing device 1. FIG. 5B is a block diagram showing a hardware configuration for executing software for realizing the functions of the time series data processing device 1. In FIGS. 5A and 5B, an acquisition unit 11 acquires time series data related to the dependent variable, time series data related to the explanatory variable candidates, keywords related to the dependent variable, and attribute information related to the explanatory variable candidates from an external device (e.g., a data server, etc.) via an input interface 100. An extraction unit 13 outputs the explanatory variables of the extraction result to an external device (e.g., a prediction device, etc.) via an output interface 101.

[0057] 5A, the processing circuit 102 may be, for example, a single circuit, a composite circuit, a programmed processor, a parallel programmed processor, an ASIC (Application Specific Integrated Circuit), an FPGA (Field-Programmable Gate Array), or a combination thereof. The functions of the acquisition unit 11, the relevance determination unit 12, and the extraction unit 13 included in the time-series data processing device 1 may be realized by separate processing circuits, or these functions may be realized together by a single processing circuit.

[0058] When the processing circuit is the processor 103 shown in FIG. 5B, the functions of the acquisition unit 11, the relevance determination unit 12, and the extraction unit 13 provided in the time series data processing device 1 are realized by software, firmware, or a combination of software and firmware. The software or firmware is written as a program and stored in the memory 104 .

[0059] The processor 103 reads and executes programs stored in the memory 104 to realize the functions of the acquisition unit 11, the relevance determination unit 12, and the extraction unit 13 included in the time series data processing device 1. For example, the time series data processing device 1 includes the memory 104 for storing a program that, when executed by the processor 103, results in the execution of steps ST1 to ST3 shown in FIG. 2. These programs cause a computer to execute the procedures or methods of the processes performed by the acquisition unit 11, the relevance determination unit 12, and the extraction unit 13. The memory 104 may be a computer-readable storage medium that stores programs for causing a computer to function as the acquisition unit 11, the relevance determination unit 12, and the extraction unit 13.

[0060] Memory 104 may be, for example, a non-volatile or volatile semiconductor memory such as RAM (Random Access Memory), ROM (Read Only Memory), flash memory, EPROM (Erasable Programmable Read Only Memory), EEPROM (Electrically-EPROM) (registered trademark), a magnetic disk, a flexible disk, an optical disk, a compact disk, a mini disk, a DVD, etc.

[0061] Some of the functions of the acquisition unit 11, the relevance determination unit 12, and the extraction unit 13 included in the time-series data processing device 1 may be realized by dedicated hardware, and the other functions may be realized by software or firmware. For example, the function of the acquisition unit 11 may be realized by a processing circuit 102 that is dedicated hardware, and the functions of the relevance determination unit 12 and the extraction unit 13 may be realized by a processor 103 reading and executing a program stored in a memory 104. In this way, the processing circuit can realize the above functions by hardware, software, firmware, or a combination of these.

[0062] As described above, the time series data processing device 1 according to the first embodiment includes an acquisition unit 11 that acquires metadata including text information on the dependent variable and attribute information on the explanatory variable candidates, and time series data of the dependent variable and the explanatory variable candidates, and an association determination unit 12 that determines a function defining the relationship between the dependent variable and the explanatory variable candidates using the metadata and the time series data. The time series data includes a statistical association between the dependent variable and the explanatory variables, and the metadata includes a semantic association between the dependent variable and the explanatory variables. This allows the time series data processing device 1 to select explanatory variables that are statistically associated with the time series data of the dependent variable and semantically related to the dependent variable as explanatory variables to be used for predicting the dependent variable.

[0063] The time-series data processing device 1 according to the first embodiment includes an extraction unit 13 that extracts first extracted explanatory variable data from explanatory variable candidates based on a comparison between the value of the function determined by the relevance determination unit 12 and a threshold value. This enables the time-series data processing device 1 to extract explanatory variables that are effective for predicting the dependent variable.

[0064] In the time series data processing device 1 according to the first embodiment, the relevance determination unit 12 determines a function for calculating a degree of relevance for extraction, which is a combination of the statistical relevance of time series data between the dependent variable and the explanatory variable candidates and the semantic relevance between the dependent variable and the explanatory variable candidates based on the metadata. The extraction unit 13 extracts first extracted explanatory variable data from the explanatory variable candidates based on a comparison between the function extraction relevance determined by the relevance determination unit 12 and a threshold. This enables the time series data processing device 1 to select explanatory variables that have a statistical relevance with the time series data of the dependent variable and a semantic relationship with the dependent variable as explanatory variables to be used for predicting the dependent variable.

[0065] In the time series data processing device 1 according to the first embodiment, the function determined by the relevance determination unit 12 includes a portion for calculating a correlation coefficient, dynamic time warping, or Euclidean distance between the time series data of the dependent variable and the explanatory variable candidates, or a combination of these, as the statistical relevance of the time series data of the dependent variable and the explanatory variable candidates, and a portion for calculating a cosine similarity between the vectorized text information of the dependent variable and the attribute information of the explanatory variable candidates as the semantic relevance of the text information of the dependent variable and the attribute information of the explanatory variable candidates. This enables the time series data processing device 1 to select explanatory variables that are statistically related to the time series data of the dependent variable and semantically related to the dependent variable, as explanatory variables to be used for predicting the dependent variable.

[0066] In the time-series data processing device 1 according to the first embodiment, the relevance determining unit 12 performs missing value processing, standardization processing, and data conversion processing on the time-series data of the dependent variable and the explanatory variable candidates, and calculates the value of the function using the processed time-series data. This allows the time-series data processing device 1 to more accurately calculate the relationship between the dependent variable and the explanatory variable candidates using the function determined by the relevance determining unit 12.

[0067] In the time-series data processing device 1 according to the first embodiment, the data conversion process is a moving average, a difference series, a logarithmic transformation, a seasonal adjustment, or a combination of these. By performing these processes or a combination of these processes on the time-series data, the time-series data processing device 1 can calculate a more accurate relevance degree for extraction.

[0068] The time-series data processing method according to the first embodiment includes step ST1 in which an acquisition unit 11 acquires metadata including text information on a dependent variable and attribute information on explanatory variable candidates, and time-series data on the dependent variable and the explanatory variable candidates, and step ST2 in which an association degree determination unit 12 determines a function that defines the relationship between the dependent variable and the explanatory variable candidates, using the metadata and the time-series data. By executing the above method, the time-series data processing device 1 can select explanatory variables that have a statistical association with the time-series data of the dependent variable and a semantic relationship with the dependent variable, as explanatory variables to be used for predicting the dependent variable.

[0069] By executing the program according to the first embodiment, the computer functions as an acquisition unit 11 that acquires metadata including text information on the dependent variable and attribute information on the explanatory variable candidates, and time-series data on the dependent variable and the explanatory variable candidates, and an association determination unit 12 that determines a function that defines the relationship between the dependent variable and the explanatory variable candidates using the metadata and the time-series data. The computer that executes the program can select explanatory variables that have a statistical association with the time-series data of the dependent variable and a semantic relationship with the dependent variable, as explanatory variables to be used for predicting the dependent variable.

[0070] Embodiment 2 The time-series data processing device according to the second embodiment uses the similarity between the explanatory variable candidates to perform hierarchical clustering of the explanatory variable candidates extracted using the extraction relevance.

[0071] (Basic configuration of time series data processing device) 6 is a block diagram showing an example of the configuration of a time series data processing device 1A according to embodiment 2. The time series data processing device 1A selects explanatory variables to be used for predicting a target variable from among a plurality of types of explanatory variables. As shown in FIG. 6, the time series data processing device 1A also includes an acquisition unit 11A, a relevance determination unit 12A, an extraction unit 13A, a similarity determination unit 14, and a grouping unit 15. For example, the time series data processing device 1A is realized by a computer. The computer has a memory in which programs constituting information processing applications for realizing the functions of the acquisition unit 11A, relevance determination unit 12A, extraction unit 13A, similarity determination unit 14, and grouping unit 15 are stored. The processor included in the computer executes the information processing application read from the memory, thereby realizing the functions of the acquisition unit 11A, the relevance determination unit 12A, the extraction unit 13A, the similarity determination unit 14, and the grouping unit 15.

[0072] (Acquisition Department) The acquisition unit 11A acquires time-series data and metadata. For example, it is assumed that the time-series data processing device 1A includes a communication unit (not shown in FIG. 6) and that this communication unit enables communication with an external device (not shown in FIG. 6). In this case, the acquisition unit 11A controls the communication unit to acquire the time-series data and metadata from the external device. The external device is, for example, a data server. Examples of communication methods used for communication with the external device include Bluetooth, Wi-Fi, Zigbee, LoRa, and NFC. The communication unit is, for example, a wireless communication module compatible with any of these communication methods.

[0073] (Relevance determination unit) The relevance determination unit 12A determines a function that defines the relationship between the objective variable and the explanatory variable candidates using the metadata and time-series data. For example, the relevance determination unit 12A determines the type of function according to the objective variable and the explanatory variable candidates from among various types of functions that calculate numerical information about the relationship between the objective variable and the explanatory variable candidates using the metadata and time-series data. The type of function may be any type that calculates numerical information when the metadata and time-series data are substituted. The determination of the function also includes selecting a trained model generated by machine learning.

[0074] The relevance determination unit 12A also determines a function for calculating a degree of relevance for extraction, which is a combination of the statistical relevance of the time-series data between the dependent variable and the explanatory variable candidates and the semantic relevance between the dependent variable and the explanatory variable candidates based on the metadata. This enables the time-series data processing device 1A to select explanatory variables that are semantically related to the dependent variable in addition to the statistical relevance with the time-series data of the dependent variable.

[0075] (Extraction part) The extraction unit 13A extracts explanatory variables from the explanatory variable candidates based on a comparison between the function value determined by the relevance determination unit 12A and a threshold. For example, the extraction unit 13A extracts explanatory variable candidates corresponding to function values ​​exceeding a threshold from among a plurality of explanatory variable candidates. This allows the time series data processing device 1A to extract explanatory variables effective for predicting the objective variable. The threshold value is a threshold value related to the value of a function that is set in advance for the extraction unit 13A, and is determined based on, for example, a prediction result of a response variable using the extracted explanatory variables.

[0076] (Similarity determining unit) The similarity determination unit 14 determines a function for calculating an extraction similarity that is a combination of the statistical similarity of the time-series data between the explanatory variables extracted by the extraction unit 13A and the semantic similarity between the explanatory variables based on the metadata of the explanatory variables extracted by the extraction unit 13A. The statistical similarity is an index that indicates the statistical closeness of the time-series data between the explanatory variables extracted by the extraction unit 13A. This similarity is a degree of similarity that takes into account the data trend, small fluctuations, or phase shifts of the waveforms constituted by the time-series data of the explanatory variable candidates. The semantic similarity is an index indicating the semantic closeness of the attribute information between the explanatory variable candidates. The attribute information of the explanatory variable candidates is related to the specific field or area to which the explanatory variable candidates belong, and can also be considered domain knowledge. Therefore, semantic similarity is also an index of the domain knowledge similarity of the attribute information between the explanatory variable candidates.

[0077] (Grouping section) The grouping unit 15 groups the first extracted explanatory variable data extracted by the extraction unit 13 using the extraction similarity calculated from the function determined by the similarity determination unit 14, and outputs explanatory variables of representative data for each group. Hereinafter, the explanatory variables that are representative data of the group will be referred to as second extracted explanatory variable data. The grouping performed by the grouping unit 15 on the first extracted explanatory variable data is a hierarchical grouping using extraction similarities. Furthermore, the second extracted explanatory variable data that is representative data of the group is, for example, an explanatory variable that has the highest average extraction similarity among other explanatory variables that belong to the same group. For example, the second extracted explanatory variable data extracted by the time-series data processing device 1A is output to a prediction device, and the prediction device predicts the dependent variable using the second extracted explanatory variable data.

[0078] Next, a time-series data processing method according to the second embodiment will be described. FIG. 7 is a flowchart showing a time series data processing method according to the second embodiment, and shows a series of processes performed by the time series data processing device 1A. First, the acquiring unit 11A acquires time-series data on the dependent variable, time-series data on the explanatory variable candidates, and metadata including keywords on the dependent variable and attribute information on the explanatory variable candidates (step ST1A). For example, the time-series data on the dependent variable and the time-series data on the explanatory variable candidates are data sets acquired at regular intervals.

[0079] As a process of determining the degree of association, the degree of association determination unit 12A determines a function that defines the relationship between the objective variable and the explanatory variable candidates using the metadata and the time-series data (step ST2A). For example, the relevance determination unit 12A determines a function for calculating an extraction relevance consisting of a combination of the statistical relevance of the time-series data between the objective variable and the explanatory variable candidate and the semantic relevance between the objective variable and the explanatory variable candidate based on the metadata.

[0080] The relevance determination unit 12A performs missing value processing, standardization processing, and data conversion processing on the time-series data of the dependent variable and the explanatory variable candidates, as in the first embodiment, and calculates the value of the function using the processed time-series data. For example, the relevance determination unit 12A performs the above processing on the time-series data related to the dependent variable and the time-series data related to the explanatory variable candidates acquired by the acquisition unit 11A.

[0081] The relevance determination unit 12A calculates the extraction relevance as a function value for the determined function using the processed time-series data. The extraction relevance calculated by the relevance determination unit 12A is output to the extraction unit 13A. The extraction unit 13A extracts first extracted explanatory variable data from the explanatory variable candidates based on a comparison between the extraction relevance and a threshold (step ST3A). For example, the extraction unit 13A extracts explanatory variables corresponding to the extraction relevance exceeding the threshold from the multiple explanatory variable candidates.

[0082] The similarity determination unit 14 determines a function for calculating a similarity for extraction that is a combination of the statistical similarity of the time-series data between the explanatory variables extracted by the extraction unit 13A and the semantic similarity between the explanatory variables based on the metadata of the explanatory variables extracted by the extraction unit 13A (step ST4A).The similarity determination unit 14 then calculates a similarity for extraction as a function value for the determined function using the attribute information and time-series data related to the explanatory variable candidates acquired by the acquisition unit 11A.The similarity for extraction calculated by the similarity determination unit 14 is output to the grouping unit 15.

[0083] (Example of similarity calculation) The function for calculating the extraction similarity is a function having a first part and a second part. The first part is for calculating the correlation coefficient, DTW, or Euclidean distance of the time series data between the explanatory variable candidates, or a combination of these, as the statistical similarity of the time series data between the explanatory variable candidates. The second part is for calculating the cosine similarity of the vectorized attribute information of the explanatory variable candidates as the semantic similarity of the attribute information between the explanatory variable candidates.

[0084] (Calculation of statistical similarity) In the first part, the statistical similarity of the time series data is calculated for each candidate explanatory variable. For example, the first part is composed of a combination of three indices for calculating statistical similarity: correlation coefficient, DTW, and Euclidean distance. In the first part, the score obtained for each indices is converted to a value between 0 (low) and 1 (high), and the statistical similarity is calculated by taking the weighted average of these scores.

[0085] The time series data (x, y) of the objective variable and the explanatory variable candidates input to the first part is normalized by the similarity determining unit 14 so that the value of each point falls within the range of 0 to 1 according to the above formula (1). The correlation coefficient, an index used to calculate statistical similarity, is a statistic that indicates the strength of the linear relationship between time-series data of candidate explanatory variables. The value of the correlation coefficient ranges from -1 to 1, with values ​​closer to 1 indicating a stronger positive correlation and values ​​closer to -1 indicating a stronger negative correlation. The similarity determination unit 14 calculates the correlation coefficient s for the pair x', y' of normalized data in accordance with the above formula (2). cor Calculate.

[0086] DTW is an index for evaluating the similarity of time series data, and is effective even when the time axes are misaligned. DTW optimally aligns two time series data to calculate the distance. The similarity determination unit 14 calculates the DTW score s for the pair of normalized data x', y', taking into account positive and negative values ​​according to the above formula (3). dtw In the above formula (3), N is the length of the time series data.

[0087] Euclidean distance is the straight-line distance between two data points, and in time series data, it is calculated by comparing values ​​at the same time point. The similarity determination unit 14 calculates the score s of the Euclidean distance for the pair x', y' of normalized data, taking into account positive and negative values, according to the above formula (4). L2 Calculate.

[0088] The similarity determination unit 14 calculates the weighted average value s of the above three indices as the statistical similarity in accordance with the above formula (5). In the above formula (5), i is a subscript indicating the three indices, namely, the correlation coefficient, the DTW, and the Euclidean distance. s i is the score of the index indicated by the subscript i.

[0089] (Calculation of semantic similarity) In the second part, semantic similarity is calculated by quantifying the degree of agreement between the attribute information of the explanatory variable candidates. If the attribute information of the explanatory variable candidates is hierarchical, the semantic similarity is calculated for each layer. For example, in the second part, attribute information between explanatory variable candidates is vectorized, and the cosine similarity of the attribute information vector is calculated as the semantic similarity. Vectorization (numerical representation) is achieved by using word2vec or BERT, etc. However, if the cosine similarity is a negative value, the vector is set to 0.

[0090] FIG. 8A is a diagram showing an example of attribute information related to explanatory variable candidates. In FIG. 8A, explanatory variable a, explanatory variable b, explanatory variable c,..., explanatory variable h, explanatory variable i,... are explanatory variable candidates. Explanatory variables a, b, and c correspond to objective variables whose keywords are "construction" and "start of construction." Explanatory variables a and b contain information belonging to "construction" and "start of construction," etc. In the attribute information of explanatory variables a and b, the major category is "construction" and the medium category is "start of construction." Explanatory variable c contains information belonging to "construction" and "order," etc. In the attribute information of explanatory variable c, the major category is "construction" and the medium category is "order."

[0091] The explanatory variable h and explanatory variable i correspond to the objective variables whose keywords are "air conditioner," "air conditioner," and "outdoor unit." The explanatory variable h includes information belonging to "machinery" corresponding to "air conditioner," "air conditioner," or "outdoor unit," etc., and includes information about its "production." As shown in FIG. 8A, the attribute information of the explanatory variable h includes the major category of "machinery" and the medium category of "production." Similarly, the explanatory variable i includes information belonging to "machinery" corresponding to "air conditioner," "air conditioner," or "outdoor unit," etc., and includes information about its "inventory." The attribute information of the explanatory variable i includes the major category of "machinery" and the medium category of "inventory."

[0092] FIG. 8B is a diagram showing an example of attribute pairs between explanatory variable candidates. The attribute pairs shown in FIG. 8B are pairs between attribute i of a certain explanatory variable candidate and attribute j of another explanatory variable candidate. "Order" of attribute i is a pair with "Construction start" of attribute j, and "Sales" of attribute i is a pair with "Inventory" of attribute j. For attribute pairs registered in advance by the user, the semantic similarity between the explanatory variable candidates corresponding to this pair is set to "1."

[0093] Fig. 8C is a diagram showing an example of weights assigned to attribute information between explanatory variable candidates. As shown in Fig. 8A, the attribute information regarding the explanatory variable candidates is divided into two hierarchical levels: major classification and medium classification, and a weight is assigned to each level. In the example shown in Fig. 8C, a weight of 0.75 is assigned to the major classification, and a weight of 0.25 is assigned to the medium classification.

[0094] 8D is a diagram showing an example of semantic similarity. In FIG. 8D, explanatory variable a, explanatory variable b, explanatory variable c, . . ., explanatory variable h, explanatory variable i, . . . are explanatory variable candidates whose attribute information has two levels, that is, a major classification and an intermediate classification. The similarity determination unit 14 calculates two semantic similarities, that is, a major classification and an intermediate classification, for one pair of explanatory variables. The pair of explanatory variable a and explanatory variable b has a major classification of "construction" and a medium classification of "construction start." Therefore, the semantic similarity of these pairs is 1. The pair of explanatory variable a and explanatory variable c and the pair of explanatory variable b and explanatory variable c are different in that the medium classification of explanatory variable a and explanatory variable b is "construction start" and explanatory variable c is "order received." However, because these were registered by a user, for example, the semantic similarity is corrected to 1. On the other hand, for example, in the pair of explanatory variables a and h, the degree of agreement in both the major classification and the medium classification is low, so the semantic similarity is 0.33.

[0095] When attribute information has multiple hierarchical levels and multiple semantic similarities corresponding to the number of hierarchical levels are calculated for one pair of explanatory variables, the similarity determination unit 14 may calculate a weighted average of these semantic similarities as the semantic similarity for this pair of explanatory variables. Furthermore, in the function determined by the similarity determination unit 14, a weighted average of the statistical similarity calculated by the first part and the semantic similarity calculated by the second part is calculated as the similarity for extraction. The similarity for extraction calculated by the similarity determination unit 14 is output to the grouping unit 15.

[0096] 7, the grouping unit 15 groups the first extracted explanatory variable data extracted by the extraction unit 13A using the similarity for extraction calculated from the function determined by the similarity determination unit 14, and outputs explanatory variables that are representative data for each group as second extracted explanatory variable data (step ST5A). The grouping unit 15 performs hierarchical clustering of the first extracted explanatory variable data using the similarity for extraction between the explanatory variables.

[0097] For example, the grouping unit 15 may perform agglomerative hierarchical clustering. Agglomerative hierarchical clustering is a clustering technique for capturing the hierarchical structure of data. This technique starts with each data point as a separate group, and then gradually combines the groups until all data points are finally combined into a single group. This process forms a hierarchical structure of groups.

[0098] 9A is a diagram showing a similarity matrix whose elements are the similarities between each pair of explanatory variables a to d. In FIG. 9A, the similarity between explanatory variable a and explanatory variable b is 0.95, the similarity between explanatory variable a and explanatory variable c is 0.74, and the similarity between explanatory variable a and explanatory variable d is 0.80. The similarity between explanatory variable b and explanatory variable c is 0.75, and the similarity between explanatory variable b and explanatory variable d is 0.96. The similarity between explanatory variable c and explanatory variable d is 0.72.

[0099] The grouping unit 15 may perform clustering using dissimilarity (=1-similarity) as an index corresponding to the distance between explanatory variables. Fig. 9B is a diagram showing the distance between groups, in which the explanatory variables are clustered according to the similarity shown in Fig. 9A. The diagram shows the clustering results when the shortest distance method is used. The grouping unit 15 uses the dissimilarity (= 1 - similarity) as the distance and a threshold value THB (= 0.10) for this distance, thereby enabling clustering of the explanatory variables a to d as shown in Fig. 9B.

[0100] Based on the clustering results, the grouping unit 15 determines the groups enclosed by the dashed lines in FIG. 9B using the similarity threshold THA (=0.90) designated by the user. The explanatory variables a, b, and d are grouped into the same group (1) because their distances are less than or equal to the threshold THB. On the other hand, the explanatory variable c is grouped into a different group (2) from group (1) because its distance (=0.25) is greater than or equal to the threshold THB. The distance between groups can be calculated using the shortest distance method (simple linkage method), the longest distance method (complete linkage method), or the group average method.

[0101] The grouping unit 15 extracts the explanatory variable having the highest average extraction similarity compared to the other explanatory variables in the group as second extracted explanatory variable data.

[0102] The second extracted explanatory variable data extracted by the time-series data processing device 1A is output to a prediction device, which predicts a dependent variable using the second extracted explanatory variable data. By executing the processes of steps ST1A to ST5A, the time-series data processing device 1A can select explanatory variables that have a statistical correlation with the time-series data of the dependent variable and a semantic relationship with the dependent variable as second extracted explanatory variable data to be used for predicting the dependent variable.

[0103] As described above, the time series data processing device 1A according to the second embodiment includes a similarity determination unit 14 that determines a function for calculating a similarity for extraction that is a combination of the statistical similarity of the time series data between the explanatory variables extracted by the extraction unit 13A and the semantic similarity between the explanatory variables based on the metadata of the explanatory variables extracted by the extraction unit 13A, and a grouping unit 15 that groups the explanatory variables extracted by the extraction unit 13 using the similarity for extraction calculated from the function determined by the similarity determination unit 14 and outputs explanatory variables of representative data for each group. This allows the time series data processing device 1A to avoid selecting semantically similar explanatory variables in addition to the statistical similarity between the explanatory variables.

[0104] In the time series data processing device 1A according to the second embodiment, the function determined by the similarity determination unit 14 includes a part for calculating the correlation coefficient, dynamic time warping, or Euclidean distance of the time series data between the explanatory variables, or a combination of these, as the statistical similarity of the time series data between the explanatory variables, and a part for calculating the cosine similarity of the vectorized attribute information of the explanatory variable candidates as the semantic similarity of the attribute information of the explanatory variable candidates. This enables the time series data processing device 1A to avoid selecting semantically similar explanatory variables in addition to the statistical similarity between the explanatory variables.

[0105] Embodiment 3 The time-series data processing device according to the third embodiment displays information such as waveforms, meta information, relevance, and similar variables of explanatory variables extracted for use in predicting a target variable, thereby presenting this information to a user.

[0106] (Basic configuration of time series data processing device) FIG. 10 is a block diagram showing an example of the configuration of a time series data processing device 1B according to the third embodiment. The time series data processing device 1B selects explanatory variables to be used for predicting a dependent variable from among a plurality of types of explanatory variables. As shown in FIG. 10, the time series data processing device 1B may be a device connected to a display device 2 via a wired or wireless connection. The display device 2 displays information output from the time series data processing device 1B.

[0107] For example, the display device 2 acquires and displays information such as waveforms, meta information, relevance, and similar variables of explanatory variables extracted for use in predicting the response variable from the time-series data processing device 1B. By referring to the information displayed on the display device 2 and operating an operation device not shown in FIG. 10, the user can select explanatory variables to be used for predicting the response variable. The display device 2 may be a display device included in the time series data processing device 1B, or may be a display unit included in a terminal communicatively connected to the time series data processing device 1B via a network.

[0108] As shown in FIG. 10 , the time series data processing device 1B includes an acquisition unit 11B, a relevance determination unit 12B, an extraction unit 13A, a similarity determination unit 14A, a grouping unit 15A, and an output unit 16. For example, the time series data processing device 1B is realized by a computer. A memory included in the computer stores a program constituting an information processing application for realizing each function of the acquisition unit 11B, the relevance determination unit 12B, the extraction unit 13A, the similarity determination unit 14A, the grouping unit 15A, and the output unit 16. A processor included in the computer executes the information processing application read from the memory, thereby realizing each function of the acquisition unit 11B, the relevance determination unit 12B, the extraction unit 13A, the similarity determination unit 14A, the grouping unit 15A, and the output unit 16.

[0109] (Acquisition Department) The acquisition unit 11B acquires time-series data and metadata. For example, it is assumed that the time-series data processing device 1B includes a communication unit (not shown in FIG. 10) and is capable of communicating with an external device (not shown in FIG. 10) via this communication unit. In this case, the acquisition unit 11B controls the communication unit to acquire time-series data and metadata from the external device. The external device is, for example, a data server. Examples of communication methods used for communication with the external device include Bluetooth, Wi-Fi, Zigbee, LoRa, and NFC. The communication unit is, for example, a wireless communication module compatible with any of these communication methods. In the third embodiment, the acquiring unit 11B outputs the acquired metadata to the output unit 16 in addition to the relevance determining unit 12B and the similarity determining unit 14A.

[0110] (Relevance determination unit) The relevance determination unit 12B determines a function that defines the relationship between the objective variable and the explanatory variable candidates using the metadata and time-series data. For example, the relevance determination unit 12B determines the type of function according to the objective variable and the explanatory variable candidates from among various types of functions that calculate numerical information about the relationship between the objective variable and the explanatory variable candidates using the metadata and time-series data. The type of function may be any type that calculates numerical information when the metadata and time-series data are substituted. The determination of the function also includes selecting a trained model generated by machine learning.

[0111] In addition, the relevance determination unit 12B determines a function for calculating an extraction relevance consisting of a combination of the statistical relevance of the time-series data between the objective variable and the explanatory variable candidate, and the semantic relevance between the objective variable and the explanatory variable candidate based on the metadata. Furthermore, the relevance determination unit 12B performs missing value processing, standardization processing, and data conversion processing on the time series data of the dependent variable and the candidate explanatory variables, as in the first embodiment, and calculates the value of the function using the processed time series data. The relevance determining unit 12B calculates an extraction relevance as a function value for the determined function using the processed time-series data, and outputs the calculated extraction relevance to the extracting unit 13A. In the third embodiment, the relevance determining unit 12B outputs the determined function value (relevance for extraction) to the extracting unit 13A, and also outputs the processed time-series data to the output unit 16.

[0112] (Extraction part) The extraction unit 13A extracts explanatory variables from the explanatory variable candidates based on a comparison between the function value determined by the relevance determination unit 12B and a threshold. For example, the extraction unit 13A extracts explanatory variable candidates corresponding to function values ​​exceeding a threshold from among a plurality of explanatory variable candidates. This allows the time series data processing device 1B to extract explanatory variables effective for predicting the objective variable. The threshold value is a threshold value related to the value of a function that is set in advance for the extraction unit 13A, and is determined based on, for example, a prediction result of a response variable using the extracted explanatory variables.

[0113] (Similarity determining unit) The similarity determination unit 14A determines a function for calculating extraction similarities, which are a combination of the statistical similarity of the time-series data between explanatory variables extracted by the extraction unit 13A and the semantic similarity between explanatory variables based on the metadata of the explanatory variables extracted by the extraction unit 13A. The statistical similarity is an index indicating the statistical closeness of the time-series data between the explanatory variables extracted by the extraction unit 13A. This similarity is a degree of similarity that takes into account the data trends, small fluctuations, or phase shifts of the waveforms formed by the time-series data of the explanatory variable candidates. The semantic similarity is an index indicating the semantic closeness of the attribute information between the explanatory variable candidates. The attribute information of the explanatory variable candidates is related to the specific field or area to which the explanatory variable candidates belong and can also be considered domain knowledge. Therefore, the semantic similarity is also an index of the domain knowledge similarity of the attribute information between the explanatory variable candidates. In the third embodiment, the similarity determining unit 14A adds the determined function value (similarity for extraction) to the grouping unit 15A and also outputs it to the output unit 16.

[0114] (Grouping section) The grouping unit 15A groups the first extracted explanatory variable data extracted by the extraction unit 13A using the extraction similarity calculated from the function determined by the similarity determination unit 14A, and outputs explanatory variables of representative data for each group. Hereinafter, the explanatory variables that are representative data of the group will be referred to as second extracted explanatory variable data. The grouping performed by the grouping unit 15A on the first extracted explanatory variable data is a hierarchical grouping using extraction similarities. Furthermore, the second extracted explanatory variable data that is representative data of the group is, for example, an explanatory variable that has the highest average extraction similarity among other explanatory variables that belong to the same group. In the third embodiment, the second extracted explanatory variable data extracted by the grouping unit 15A is output to the output unit 16.

[0115] (output section) The output unit 16 is a so-called visualization unit that displays the waveforms, metadata, relevance, or similarity related to the explanatory variables extracted by the extraction unit 13A on the display device 2. For example, the output unit 16 generates display information for displaying the second extracted explanatory variable data, the waveforms of the time-series data, the metadata, the relevance for extraction, or the similarity for extraction extracted by the grouping unit 15A, and outputs the display information to the display device 2. The display device 2 displays the second extracted explanatory variable data, the waveforms of the time-series data, the metadata, the relevance for extraction, or the similarity for extraction based on the display information acquired from the output unit 16.

[0116] (Example of display screen) The output unit 16 causes the display device 2 to display, for example, an explanatory variable list display screen including the second extracted explanatory variable data, the waveform of the time-series data, the metadata, the relevance for extraction or the similarity for extraction, an individual explanatory variable display screen, or a selected explanatory variable display screen.

[0117] FIG. 11 is a screen diagram showing a screen example (1) in the third embodiment, illustrating an explanatory variable list display screen 2A. The output unit 16 generates display information for displaying the explanatory variable list display screen 2A and outputs it to the display device 2. The display device 2 displays the explanatory variable list display screen 2A based on the display information acquired from the output unit 16. Various information related to the explanatory variables can be selected from the explanatory variable list display screen 2A by operating an operation device (not shown in FIG. 10). The operation device may be included in the time series data processing device 1B, or may be an operation unit included in a terminal communicatively connected to the time series data processing device 1B via a network.

[0118] As shown in FIG. 11 , the explanatory variable list display screen 2A displays setting fields in which information set by the user is displayed. These setting fields display, for example, a “Number of Selected Explanatory Variables” field 21, a “Number of Displayed Similar Variables” field 22, and a “Similarity Threshold” field 23. The “Number of Selected Explanatory Variables” field 21 sets the number of explanatory variables currently selected on the explanatory variable list display screen 2A (e.g., 10). The “Number of Displayed Similar Variables” field 22 sets the maximum number of similar explanatory variables that can be displayed on the explanatory variable list display screen 2A (e.g., 3). The “Similarity Threshold” field 23 sets a similarity threshold (e.g., 0.75) for determining the explanatory variables to be displayed on the explanatory variable list display screen 2A. The explanatory variable list display screen 2A displays explanatory variables (second extracted explanatory variable data) whose similarity for extraction is equal to or greater than the similarity threshold, in descending order of similarity for extraction, up to the number set in the “Number of Displayed Similar Variables” field 22.

[0119] 11, the explanatory variable list display screen 2A displays a "Selection" column, a "Meta Information" column, a "Data Name" column, a "Relevance" column, and a "Similar Variable" column related to the second extracted explanatory variable data extracted by the grouping unit 15. Explanatory variables are selected by checking the checkboxes in the "Selection" column using the operation device. 11, the explanatory variable list display screen 2A shows that the explanatory variable a is selected because the check box 24 is checked, and the explanatory variable b is selected because the check box 25 is checked. Furthermore, because the explanatory variable b is selected, the check box 26 is also checked in conjunction with the selection.

[0120] The attribute information, "data name," and "relevance" displayed in the "major category" and "medium category" of the "meta information" column 27 can be selected using the operating device, and if the contents of the column are text, they are sorted in the order of the text (for example, A to Z), and if the contents of the column are numbers (for example, relevance), they are sorted and displayed in descending order of the numbers. Furthermore, by checking a check box 28 using the operating device, explanatory variables similar to explanatory variable a are displayed in a display field 29 in the number set in the "number of similar variables to display" field 22. For example, information on explanatory variables b, g, and m, which are not classified into the same group as explanatory variable a but are similar in terms of the statistics or metadata of the time-series data, is displayed in the display field 29.

[0121] Furthermore, when a cell containing "explanatory variable a" is selected using the operation device, the operation information is output to the output unit 16. In response to the operation information, the output unit 16 generates display information for displaying an explanatory variable individual display screen 2B related to the explanatory variable a, and outputs the generated display information to the display device 2. Based on this display information, the display device 2 displays the explanatory variable individual display screen 2B related to the explanatory variable a.

[0122] FIG. 12 is a screen diagram showing a screen example (2) in the third embodiment, and shows an explanatory variable individual display screen 2B. The output unit 16 generates display information for displaying the explanatory variable individual display screen 2B for one explanatory variable, and outputs it to the display device 2. The display device 2 displays the explanatory variable individual display screen 2B based on the display information acquired from the output unit 16. The explanatory variable individual display screen 2B displays various information related to an explanatory variable selected using the operation device. For example, it is assumed that the cell for "explanatory variable a" is selected on the explanatory variable list display screen 2A, and thereby the explanatory variable individual display screen 2B related to explanatory variable a is displayed.

[0123] As shown in FIG. 12, the explanatory variable individual display screen 2B displays a setting field 21A in which information set by the user is displayed, an explanatory variable information field 22A, and a waveform field 23A. The setting field 21A displays a "Number of Selected Explanatory Variables" field, a "Number of Displayed Similar Variables" field, a "Similarity Threshold" field, and a "Similar Variable Waveform Display" field with check boxes 211A. The "Number of Selected Explanatory Variables" field sets the number of explanatory variables currently selected on the individual explanatory variable display screen 2B (e.g., 10). The "Number of Displayed Similar Variables" field sets the maximum number of similar explanatory variables that can be displayed on the individual explanatory variable display screen 2B (e.g., 3). The "Similarity Threshold" field sets a similarity threshold (e.g., 0.75) for determining which explanatory variables to display on the individual explanatory variable display screen 2B. For each explanatory variable, the individual explanatory variable display screen 2B displays explanatory variables (second extracted explanatory variable data) whose similarity for extraction is equal to or greater than the similarity threshold, in descending order of similarity for extraction, up to the number set in the "Number of Displayed Similar Variables" field.

[0124] The waveform 231A of the time-series data of explanatory variable a selected for individual display and the corresponding response variable are always displayed. Cell 221A in the explanatory variable information field 22A displays "explanatory variable a," which is the explanatory variable to be individually displayed. Checking checkbox 211A displays the waveforms of similar explanatory variables b, g, and m linked to explanatory variable a. Since explanatory variable b is selected on the explanatory variable list display screen 2A shown in FIG. 11, checkbox 222A in the selection field in the explanatory variable information field 22A is also checked in conjunction with the selection. Display field 223A displays information on explanatory variables b, g, and m that are not classified into the same group as explanatory variable a but are semantically similar in terms of statistical or metadata information of the time-series data.

[0125] FIG. 13 is a screen diagram showing a screen example (3) in the third embodiment, illustrating a selected explanatory variable display screen 2C. The output unit 16 generates display information for displaying the selected explanatory variable display screen 2C and outputs it to the display device 2. The display device 2 displays the selected explanatory variable display screen 2C based on the display information acquired from the output unit 16. The selected explanatory variable display screen 2C displays various information related to explanatory variables selected using the operation device. For example, by checking multiple check boxes in the selection column on the explanatory variable list display screen 2A, the selected explanatory variable display screen 2C related to the explanatory variables selected by these check boxes is displayed.

[0126] As shown in FIG. 13, the selected explanatory variable display screen 2C displays a setting field 21B in which information set by the user is displayed, an explanatory variable information field 22B, and a waveform field 23B. The number of explanatory variables being selected is set in the setting field 21 B. The selected explanatory variable display screen 2 C displays the waveform, relevance, meta information, and data name for the selected explanatory variable. In the initial display, it is assumed that all check boxes in the selection fields of the explanatory variable information field 22B are checked. In the waveform field 23B, waveforms of the time-series data of the response variable and explanatory variables whose selection fields have checked check boxes in the explanatory variable information field 22B are displayed. In addition, explanatory variables can be selected from the selected explanatory variable display screen 2C. For example, explanatory variables can be prevented from being used by unchecking the checkbox in the "Select" column of the explanatory variable information column 22B corresponding to the explanatory variable that has been determined not to be used. In this case, the waveform of the time-series data corresponding to the unchecked explanatory variable is hidden in the waveform column 23B. As described above, by displaying the explanatory variable list display screen 2A, the individual explanatory variable display screen 2B, or the selected explanatory variable display screen 2C on the display device 2, a user who refers to these screens can efficiently select explanatory variables to be used in predicting the target variable.

[0127] As described above, the time-series data processing device 1B according to the third embodiment includes the output unit 16 that displays the waveform, metadata, relevance, or similarity related to the explanatory variables extracted by the extraction unit 13A on the display device 2. By referring to the information displayed on the display device 2, the user can efficiently select explanatory variables to be used for predicting the dependent variable.

[0128] Embodiment 4 The time-series data processing device according to the fourth embodiment extracts explanatory variables related to the dependent variable by an independence test, even when other explanatory variables are conditioned.

[0129] (Basic configuration of time series data processing device) FIG. 14 is a block diagram showing an example of the configuration of a time series data processing device 1C according to the fourth embodiment. The time series data processing device 1C selects explanatory variables to be used for predicting a dependent variable from among a plurality of types of explanatory variables. As shown in FIG. 14, the time series data processing device 1C may be a device connected to a display device 2 via a wired or wireless connection. The display device 2 displays information output from the time series data processing device 1C.

[0130] For example, the display device 2 acquires and displays information such as the waveforms, meta information, relevance, and similar variables of the explanatory variables extracted for use in predicting the response variable from the time-series data processing device 1C. By referring to the information displayed on the display device 2 and operating an operation device not shown in FIG. 14, the user can select explanatory variables to be used for predicting the response variable. The display device 2 may be a display device included in the time series data processing device 1C, or may be a display unit included in a terminal communicatively connected to the time series data processing device 1C via a network.

[0131] The time series data processing device 1C has a configuration in which a testing unit 17 is added to the time series data processing device 1B, but is not limited to this. For example, the time series data processing device 1C may have a configuration in which a testing unit 17 is added to the time series data processing device 1, or may have a configuration in which a testing unit 17 is added to the time series data processing device 1A.

[0132] The time series data processing device 1C includes an acquisition unit 11B, a relevance determination unit 12C, an extraction unit 13A, a similarity determination unit 14B, a grouping unit 15B, an output unit 16A, and a testing unit 17. For example, time-series data processing device 1C is realized by a computer. A memory included in the computer stores programs constituting an information processing application for realizing the functions of acquisition unit 11B, degree-of-association determination unit 12C, extraction unit 13A, similarity determination unit 14B, grouping unit 15B, output unit 16A, and testing unit 17. A processor included in the computer executes the information processing application read from the memory, thereby realizing the functions of acquisition unit 11B, degree-of-association determination unit 12C, extraction unit 13A, similarity determination unit 14B, grouping unit 15B, output unit 16A, and testing unit 17.

[0133] (Acquisition Department) The acquisition unit 11B acquires time-series data and metadata. For example, it is assumed that the time-series data processing device 1C includes a communication unit (not shown in FIG. 14) and is capable of communicating with an external device (not shown in FIG. 14) via this communication unit. In this case, the acquisition unit 11B controls the communication unit to acquire time-series data and metadata from the external device. The external device is, for example, a data server. Examples of communication methods used for communication with the external device include Bluetooth, Wi-Fi, Zigbee, LoRa, and NFC. The communication unit is, for example, a wireless communication module compatible with any of these communication methods. In the fourth embodiment, the acquisition unit 11B outputs the acquired metadata to the test unit 17 in addition to the relevance determination unit 12C, the similarity determination unit 14B, and the output unit 16A.

[0134] (Relevance determination unit) The relevance determination unit 12C determines a function that defines the relationship between the objective variable and the explanatory variable candidates using the metadata and time-series data. For example, the relevance determination unit 12C determines the type of function according to the objective variable and the explanatory variable candidates from among various types of functions that calculate numerical information about the relationship between the objective variable and the explanatory variable candidates using the metadata and time-series data. The function type may be any type that calculates numerical information when the metadata and time-series data are substituted. The determination of the function also includes selecting a trained model generated by machine learning.

[0135] In addition, the relevance determination unit 12C determines a function for calculating an extraction relevance consisting of a combination of the statistical relevance of the time-series data between the objective variable and the explanatory variable candidate, and the semantic relevance between the objective variable and the explanatory variable candidate based on the metadata. Furthermore, the relevance determination unit 12C performs missing value processing, standardization processing, and data conversion processing on the time series data of the dependent variable and the candidate explanatory variables, as in embodiment 1, and calculates the value of the function using the processed time series data. The relevance determining unit 12C calculates an extraction relevance as a function value for the determined function using the processed time-series data, and outputs the calculated extraction relevance to the extracting unit 13A. In the fourth embodiment, the relevance determining unit 12C outputs the determined function value (relevance for extraction) to the extracting unit 13A, and also outputs the processed time-series data to the output unit 16A and the testing unit 17.

[0136] (Extraction part) The extraction unit 13A extracts explanatory variables from the explanatory variable candidates based on a comparison between the function value determined by the relevance determination unit 12C and a threshold. For example, the extraction unit 13A extracts explanatory variable candidates corresponding to function values ​​exceeding a threshold from among multiple explanatory variable candidates. This allows the time series data processing device 1C to extract explanatory variables effective for predicting the objective variable. The threshold value is a threshold value related to the value of a function that is set in advance for the extraction unit 13A, and is determined based on, for example, a prediction result of a response variable using the extracted explanatory variables.

[0137] (Similarity determining unit) The similarity determination unit 14B determines a function for calculating extraction similarities, which are a combination of the statistical similarity of the time-series data between the explanatory variables extracted by the extraction unit 13A and the semantic similarity between the explanatory variables based on the metadata of the explanatory variables extracted by the extraction unit 13A. The statistical similarity is an index indicating the statistical closeness of the time-series data between the explanatory variables extracted by the extraction unit 13A. This similarity is a degree of similarity that takes into account the data trends, small fluctuations, or phase shifts of the waveforms formed by the time-series data of the explanatory variable candidates. The semantic similarity is an index indicating the semantic closeness of the attribute information between the explanatory variable candidates. The attribute information of the explanatory variable candidates is related to the specific field or area to which the explanatory variable candidates belong and can also be considered domain knowledge. Therefore, the semantic similarity is also an index of the domain knowledge similarity of the attribute information between the explanatory variable candidates. In the fourth embodiment, the similarity determining unit 14B adds the determined function value (similarity for extraction) to the grouping unit 15B and also outputs it to the output unit 16A.

[0138] (Grouping section) The grouping unit 15B groups the first extracted explanatory variable data extracted by the extraction unit 13A using the extraction similarity calculated from the function determined by the similarity determination unit 14B, and outputs the explanatory variables of the representative data for each group. Hereinafter, the explanatory variables that are representative data of the group will be referred to as second extracted explanatory variable data. The grouping performed by the grouping unit 15B on the first extracted explanatory variable data is a hierarchical grouping using extraction similarities.

[0139] Furthermore, the second extracted explanatory variable data that is representative data of the group is, for example, an explanatory variable that has the highest average extraction similarity among other explanatory variables that belong to the same group. In the fourth embodiment, the second extracted explanatory variable data extracted by the grouping unit 15B is output to the testing unit 17.

[0140] (output section) The output unit 16A is a so-called visualization unit that displays the waveform, metadata, relevance, or similarity related to the explanatory variable extracted by the testing unit 17 (hereinafter referred to as third extracted explanatory variable data) on the display device 2. For example, the output unit 16A generates display information for displaying the third extracted explanatory variable data, the waveform of the time-series data, the metadata, the relevance for extraction, or the similarity for extraction extracted by the testing unit 17, and outputs it to the display device 2. The display device 2 displays the third extracted explanatory variable data, the waveform of the time-series data, the metadata, the relevance for extraction, or the similarity for extraction based on the display information acquired from the output unit 16.

[0141] (Testing Department) The testing unit 17 extracts explanatory variables associated with the target variable through an independence test. For example, the testing unit 17 evaluates the degree of association for extraction when other explanatory variables are conditioned on the second extracted explanatory variable data extracted by the grouping unit 15B using a conditional independence test. The conditional independence test is a statistical method for evaluating whether a certain explanatory variable depends on other variables. In particular, the conditional independence test is used to evaluate the dependency when other explanatory variables (covariates) are conditioned on the data. A conditional independence test evaluates whether certain variables X and Y are independent conditional on another variable (covariate) Z.

[0142] The testing unit 17 uses a conditional independence test on the second extracted explanatory variable data to calculate a p-value that represents the relevance when other explanatory variables are conditioned, according to the following formula (6), and extracts explanatory variables whose p-value is equal to or greater than a threshold value as third extracted explanatory variable data. In the following formula (6), S is a set of indexes of explanatory variables after grouping, y is a response variable, and x i is an explanatory variable. ci is a conditional independence test value, which is a function based on, for example, partial correlation coefficient, conditional mutual information, etc. p i is the response variable y and the explanatory variable xi The p-value of the test statistic for the two variables is the p-value. The closer it is to 0, the less independent the variables are. th is the p-value threshold. p i =ci(y,x i ,{x j |j∈S\i}) (6)

[0143] Next, the operation of the testing unit 17 will be described. FIG. 15 is a flowchart showing the operation of the testing unit 17. The testing unit 17 calculates a test statistic according to the above formula (6) using the time-series data processed by the relevance determining unit 12C and the second extracted explanatory variable data extracted by the grouping unit 15B (step ST1B).

[0144] Next, the test unit 17 calculates the objective variable y and the explanatory variable x i It is checked whether the p-value of the test statistic has been calculated for all pairs with (step ST2B). If there is a pair for which the test statistic has not been calculated (step ST2B; NO), the process returns to step ST1B and the test statistic for the remaining pairs is calculated. On the other hand, if the test statistic has been calculated for all pairs (step ST2B; YES), p is calculated according to the above formula (6). i has been calculated.

[0145] The test section 17 is p i Maximum value of < threshold p th It is confirmed whether or not this is the case (step ST3B). p i Maximum value of p≧threshold p th If so (step ST3B; NO), the testing unit 17 selects p from the index set S. i The explanatory variable i corresponding to the maximum value of is removed (step ST4B), and the process returns to step ST1B. On the other hand, p i Maximum value of < threshold p th If so (step ST3B; YES), the testing unit 17 ends the series of processes shown in FIG. 15, and sets the explanatory variables corresponding to the indexes included in the index set S as third extracted explanatory variable data.

[0146] As described above, the time series data processing device 1C according to the fourth embodiment includes the testing unit 17 that extracts explanatory variables associated with a target variable by an independence test. By extracting explanatory variables by an independence test, the time series data processing device 1C can select statistically valid explanatory variables.

[0147] Various aspects of the present disclosure are summarized below as appendices.

[0148] (Appendix 1) an acquisition unit that acquires metadata including text information on a dependent variable and attribute information on explanatory variable candidates, and time-series data of the dependent variable and the explanatory variable candidates; a degree of association determination unit that determines a function that defines a relationship between the objective variable and the explanatory variable candidates using the metadata and the time-series data. A time series data processing device comprising: (Appendix 2) an extraction unit that extracts explanatory variables from the explanatory variable candidates based on a comparison between the value of the function determined by the relevance determination unit and a threshold value; 2. The time-series data processing device according to claim 1, (Appendix 3) the relevance determination unit determines a function for calculating a degree of relevance for extraction, the degree of relevance being a combination of a statistical relevance of the time-series data between the objective variable and the explanatory variable candidate and a semantic relevance between the objective variable and the explanatory variable candidate based on the metadata; The extraction unit extracts the explanatory variables from the explanatory variable candidates based on a comparison between the extraction relevance calculated from the function determined by the relevance determination unit and the threshold value. 3. The time-series data processing device according to claim 2, (Appendix 4) a similarity determination unit that determines a function for calculating a similarity for extraction that is a combination of a statistical similarity of the time-series data between the explanatory variables extracted by the extraction unit and a semantic similarity between the explanatory variables based on the metadata of the explanatory variables extracted by the extraction unit; a grouping unit that groups the explanatory variables extracted by the extraction unit using the similarity for extraction calculated from the function determined by the similarity determination unit, and outputs the explanatory variables of representative data for each group. 4. The time-series data processing device according to claim 3, (Appendix 5) an output unit that displays on a display device the waveform, the metadata, the relevance, or the similarity related to the explanatory variables extracted by the extraction unit; 5. The time-series data processing device according to claim 3 or 4. (Appendix 6) A testing unit is provided that extracts the explanatory variables associated with the objective variable by an independence test. 6. The time series data processing device according to any one of Supplementary Note 1 to Supplementary Note 5, (Appendix 7) The function determined by the relevance determination unit is a part for calculating a correlation coefficient, a dynamic time warping coefficient, or a Euclidean distance, or a combination thereof, of the time series data between the dependent variable and the explanatory variable candidate as a statistical relevance of the time series data between the dependent variable and the explanatory variable candidate; a part for calculating a cosine similarity between the vectorized text information of the objective variable and the attribute information of the explanatory variable candidate as a semantic relevance between the text information of the objective variable and the attribute information of the explanatory variable candidate. 4. The time series data processing device according to claim 1, wherein: (Appendix 8) The function determined by the similarity determination unit is a part for calculating a correlation coefficient, a dynamic time warping coefficient, or a Euclidean distance of the time series data between the explanatory variables, or a combination thereof, as a statistical similarity of the time series data between the explanatory variables; a part for calculating the cosine similarity of the vectorized attribute information of the explanatory variable candidates as the semantic similarity of the attribute information of the explanatory variable candidates; 5. The time-series data processing device according to claim 4, (Appendix 9) The relevance determination unit performs missing value processing, standardization processing, and data conversion processing on the time series data of the objective variable and the candidate explanatory variables acquired by the acquisition unit, and calculates a value of a function using the processed time series data. 9. The time series data processing device according to any one of Supplementary Note 1 to Supplementary Note 8, (Appendix 10) The data transformation process is a differential series, a logarithmic transformation, a seasonal adjustment, or a combination thereof. 10. The time-series data processing device according to claim 9, (Appendix 11) A time series data processing method by a time series data processing device, comprising: an acquiring unit acquiring metadata including text information on a dependent variable and attribute information on explanatory variable candidates, and time-series data on the dependent variable and the explanatory variable candidates; a step in which an association degree determination unit determines a function that defines a relationship between the objective variable and the explanatory variable candidates, using the metadata and the time-series data. A time series data processing method comprising: (Appendix 12) Computer, an acquisition unit that acquires metadata including text information on a dependent variable and attribute information on explanatory variable candidates, and time-series data of the dependent variable and the explanatory variable candidates; a degree of association determination unit that determines a function that defines a relationship between the objective variable and the explanatory variable candidates using the metadata and the time-series data; A program to function as a

[0149] It is possible to combine the embodiments, modify any of the components of the embodiments, or omit any of the components of the embodiments. [Industrial Applicability]

[0150] The time-series data processing device according to the present disclosure can be used in, for example, various systems that handle time-series data. [Explanation of symbols]

[0151] 1, 1A, 1B, 1C time series data processing device, 2 display device, 2A explanatory variable list display screen, 2B explanatory variable individual display screen, 2C selected explanatory variable display screen, 11, 11A, 11B acquisition unit, 12, 12A, 12B, 12C relevance determination unit, 13, 13A extraction unit, 14, 14A, 14B similarity determination unit, 15, 15A, 15B grouping unit, 16, 16A output unit, 17 testing unit, 21, 21A, 21B, 22, 22A, 22B, 23, 23A, 23B, 27, 29, 223A column, 24, 25, 26, 28, 211A, 222A check box, 100 input interface, 101 output interface, 102 processing circuit, 103 processor, 104 memory, 221A Cell, 231A waveform.

Claims

1. an acquisition unit that acquires metadata including text information on a dependent variable and attribute information on explanatory variable candidates, the metadata including attribute information having one or more hierarchical levels of different granularity, and time-series data on the dependent variable and the explanatory variable candidates; a relevance determination unit that determines, using the metadata and the time-series data, a function that defines a relationship for calculating a relevance for extraction, the relevance being a combination of a statistical relevance of the time-series data between the objective variable and the explanatory variable candidate, and a semantic relevance between words included in text information of the objective variable and words included in attribute information of the explanatory variable candidate, based on the metadata; an extraction unit that extracts explanatory variables from the explanatory variable candidates based on a comparison between the degree of relevance for extraction calculated from the function determined by the degree of relevance determination unit and a threshold value. A time series data processing device comprising:

2. a similarity determination unit that determines a function for calculating an extraction similarity that is a combination of a statistical similarity of the time-series data between the explanatory variables extracted by the extraction unit and a semantic similarity between words included in attribute information of the explanatory variables based on the metadata of the explanatory variables extracted by the extraction unit; a grouping unit that groups the explanatory variables extracted by the extraction unit using the similarity for extraction calculated from the function determined by the similarity determination unit, and outputs the explanatory variables of representative data for each group.

2. The time series data processing device according to claim 1,

3. an output unit that displays on a display device the waveform, the metadata, the relevance, or the similarity related to the explanatory variables extracted by the extraction unit; 3. The time series data processing device according to claim 2.

4. A test unit is provided that extracts the explanatory variables associated with the target variable when the other explanatory variables are conditioned by an independence test.

4. The time series data processing device according to claim 3.

5. The function determined by the relevance determination unit is a part for calculating a correlation coefficient, a dynamic time warping coefficient, or a Euclidean distance, or a combination thereof, of the time series data between the dependent variable and the explanatory variable candidate as a statistical relevance of the time series data between the dependent variable and the explanatory variable candidate; a portion for calculating a cosine similarity representing a semantic relevance between words included in text information of the objective variable, which has been vectorized into a numerical vector representing the meaning of words included in the metadata, and words included in attribute information of the explanatory variable candidate, as a semantic relevance between the text information of the objective variable and the attribute information of the explanatory variable candidate.

2. The time series data processing device according to claim 1,

6. The function determined by the similarity determination unit is a part for calculating a correlation coefficient, a dynamic time warping coefficient, or a Euclidean distance of the time series data between the explanatory variables, or a combination thereof, as a statistical similarity of the time series data between the explanatory variables; a part for calculating a cosine similarity representing a semantic similarity between words included in the attribute information of the explanatory variable candidates vectorized into a numerical vector representing the meaning of the words included in the metadata as a semantic similarity between the attribute information of the explanatory variable candidates 3. The time series data processing device according to claim 2.

7. The relevance determination unit performs missing value processing, standardization processing, and data conversion processing on the time series data of the objective variable and the candidate explanatory variables acquired by the acquisition unit, and calculates a value of a function using the time series data after the missing value processing, the standardization processing, and the data conversion processing.

7. The time series data processing device according to claim 1, wherein the time series data processing device is a time series data processing device.

8. The data transformation process is a differential series, a logarithmic transformation, a seasonal adjustment, or a combination thereof.

8. The time series data processing device according to claim 7,

9. A time series data processing method by a time series data processing device, comprising: an acquiring unit acquiring metadata including text information on a dependent variable and attribute information on explanatory variable candidates, the metadata including attribute information having one or more hierarchical levels of different granularity, and time-series data on the dependent variable and the explanatory variable candidates; a step in which the relevance determination unit determines, using the metadata and the time-series data, a function for calculating a degree of relevance for extraction, the degree of relevance being a combination of a statistical degree of relevance of the time-series data between the objective variable and the explanatory variable candidate, and a semantic degree of relevance between words included in text information of the objective variable and words included in attribute information of the explanatory variable candidate, based on the metadata; an extracting unit extracting an explanatory variable from the explanatory variable candidates based on a comparison between the degree of relevance for extraction calculated from the function determined by the degree of relevance determining unit and a threshold value; A time series data processing method comprising:

10. Computer, an acquisition unit that acquires metadata including text information on a response variable and attribute information on explanatory variable candidates, the metadata including attribute information having one or more hierarchical levels of different granularity, and time-series data on the response variable and the explanatory variable candidates; a relevance determination unit that determines, using the metadata and the time-series data, a function that defines a relationship for calculating a relevance for extraction, the relevance being a combination of a statistical relevance of the time-series data between the objective variable and the explanatory variable candidate, and a semantic relevance between a word included in text information of the objective variable and a word included in attribute information of the explanatory variable candidate, based on the metadata; an extraction unit that extracts explanatory variables from the explanatory variable candidates based on a comparison between the extraction relevance calculated from the function determined by the relevance determination unit and a threshold value; A program to function as a

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