Processing device, processing method, and program

Abstract

A processing device 1 is provided with: a calculation unit 22 that calculates the degree of deviation between two sets of time-series data among a plurality of sets of time-series data using each of a plurality of analysis methods, and calculates, for each combination of two sets of time-series data among the plurality of sets of time-series data, an evaluation value for the variation of each degree of deviation calculated using each of the plurality of analysis methods; and an extraction unit 23 that extracts a combination of two sets of time-series data for which the evaluation value satisfies a prescribed condition.

Description

[Technical field]

[0001] The present invention relates to a processing device, a processing method, and a program. [Background technology]

[0002] In today's world where a wide variety of data is available, it is important to develop appropriate solutions through appropriate analysis of the data. Values ​​are now diversified, and the meaning derived from one piece of data may have a different meaning to another.

[0003] Data scientists also analyze data from multiple perspectives based on their past work experience. Analyzing large amounts of data places a burden on data scientists, and important data may be overlooked. In particular, data scientists with little experience may not be able to properly analyze large amounts of data and may not be able to identify data that is difficult to judge. Not being able to properly analyze data that is difficult to judge may prevent them from contributing to the creation of appropriate solutions.

[0004] There is a technology to support the analysis of time-series data (Patent Document 1). Patent Document 1 calculates an index value indicating the amount of change over time of each piece of data within a specified period from a specified time for each piece of time-series data that has a common data type, and displays the pieces of time-series data in the order according to the calculated index values. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] JP 2018-25891 A Summary of the Invention [Problem to be solved by the invention]

[0006] By extracting data that is difficult for data scientists to judge using computer processing, the data scientists can identify data that requires focused analysis from a large amount of data, and this is expected to enable efficient analysis by the data scientists. However, Patent Document 1 does not extract data that is difficult to judge.

[0007] The present invention has been made in consideration of the above circumstances, and an object of the present invention is to provide a technology that can extract data that is difficult for data scientists to judge and support efficient analysis. [Means for solving the problem]

[0008] A processing device according to one embodiment of the present invention includes a calculation unit that calculates a deviation between two pieces of time series data among a plurality of time series data by each of a plurality of analysis methods, and calculates an evaluation value of the deviation between each combination of two pieces of time series data among the plurality of time series data by each of the plurality of analysis methods, an extraction unit that extracts a combination of two pieces of time series data whose evaluation value satisfies a predetermined condition, and an output unit that outputs the extracted combination.

[0009] In one embodiment of the processing method of the present invention, a computer calculates a deviation between two pieces of time series data among a plurality of pieces of time series data using each of a plurality of analysis methods, calculates an evaluation value of the deviation between each combination of two pieces of time series data among the plurality of pieces of time series data calculated using each of the plurality of analysis methods, extracts a combination of two pieces of time series data whose evaluation value satisfies a predetermined condition, and outputs the extracted combination.

[0010] One aspect of the present invention is a program for causing a computer to function as the processing device. Effect of the Invention

[0011] According to the present invention, it is possible to provide a technology that can extract data that is difficult for data scientists to judge and support efficient analysis. [Brief description of the drawings]

[0012] [Figure 1] FIG. 1 is a diagram illustrating functional blocks of a processing device according to an embodiment of the present invention. [Diagram 2] FIG. 2 is a diagram illustrating an example of the data structure and data of the parameter data. [Diagram 3] FIG. 3 is a diagram illustrating an example of the data structure and data of a time-series data group. [Figure 4] FIG. 4 is a diagram illustrating an example of the data structure of evaluation value data and the data. [Diagram 5] FIG. 5 is a diagram illustrating an example of the data structure of the extracted data and the data. [Figure 6] FIG. 6 is a diagram illustrating an example of the evaluation value. [Figure 7] FIG. 7 is a diagram illustrating an example of the time-series data. [Figure 8] FIG. 8 is a flow chart of the processing device. [Figure 9] FIG. 9 is a flowchart of the update process. [Figure 10] FIG. 10 is a diagram for evaluating the evaluation value calculated by the processing device according to the embodiment of the present invention. [Figure 11] FIG. 11 is a diagram illustrating the hardware configuration of a computer used in the processing device. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0013] Hereinafter, an embodiment of the present invention will be described with reference to the drawings. In the description of the drawings, the same parts are given the same reference numerals and the description will be omitted.

[0014] (Processing device) A processing device 1 according to an embodiment of the present invention extracts, from among a plurality of time series data, a combination of time series data in which the judgment of whether two pieces of time series data are similar or dissimilar varies from person to person, or is considered impossible, as difficult-to-judge data. By extracting difficult-to-judge data, the processing device 1 can identify data that requires focused analysis from a large amount of data, thereby realizing efficient analysis of large amounts of data. Time series data is data that specifies the change in a certain value over time. The embodiment of the present invention will be described using time series data of the Consumer Price Index (price index by item) provided by the Statistics Bureau of the Ministry of Internal Affairs and Communications.

[0015] 1, the processing device 1 includes each of the data, such as parameter data 11, analysis method data 12, time-series data group 13, deviation data 14, evaluation value data 15, and extracted data 16, and each of the functions of a generating unit 21, a calculating unit 22, an extracting unit 23, an updating unit 24, and an output unit 25. Each of the data is stored in a storage device such as a memory 902 or a storage 903. Each of the functions is implemented in a CPU 901.

[0016] The parameter data 11 associates selectable options with each parameter used in an analysis method for calculating the degree of discrepancy between two time series data. The identifiers of the parameters specified by the parameter data 11 and the options therefor differ depending on the analysis method.

[0017] Parameter data 11 shown in FIG. 2 indicates options for each parameter used in an analysis method for determining the dissimilarity of two time series data. The parameter data 11 associates an identifier that identifies a parameter with an option that can be selected for each parameter. For example, the options for the parameter "difference (value)" are "absolute value," "amount of change," and "rate of change." The options for the parameter "vertical axis" are "no transformation" and "logarithmic transformation."

[0018] The analysis method data 12 is data for specifying a plurality of analysis methods for calculating the degree of discrepancy between two time series data. In the embodiment of the present invention, the plurality of analysis methods are specified by selecting one of the options for each parameter in the parameter data 11. The analysis method data 12, for example, associates an identifier of an analysis method with a parameter used in the analysis method.

[0019] The time series data group 13 is a population of data from which the processing device 1 extracts difficult-to-assess data. As shown in Fig. 3, the time series data group 13 includes a plurality of time series data. In the embodiment of the present invention, the time series data group 13 includes price fluctuation data for each of a plurality of items.

[0020] The deviation data 14 is data resulting from calculation by the calculation unit 22 of the deviation between two pieces of time series data in the time series data group 13 using each analysis method specified by the analysis method data 12. The deviation data 14 associates each identifier of the two pieces of time series data for which the deviation is calculated, an identifier of the analysis method, and the calculated deviation.

[0021] The evaluation value data 15 is data of evaluation values ​​of deviations calculated by a plurality of analysis methods for two time series data. As shown in Fig. 4, the evaluation value data 15 associates each identifier of the two time series data with an evaluation value of deviations calculated for a combination of the data. In Fig. 4, the evaluation value data 15 also associates the deviations in each analysis method with each identifier of the two time series data.

[0022] The extracted data 16 identifies a combination of two time series data with a high evaluation value. The extracted data 16 includes a combination of identifiers of the two time series data as shown in Fig. 5. In the example shown in Fig. 5, the extracted data includes two combinations. The extracted data 16 is generated from the evaluation value data 15 by the extraction unit 23.

[0023] The generating unit 21 generates, as a plurality of analysis methods, each analysis method in which one of the options of the parameters for calculating the deviation degree of two time series data is selected. The generating unit 21 selects one option from each parameter identified in the parameter data 11, identifies a plurality of analysis methods, and generates analysis method data 12. In the case of the parameter data shown in FIG. 2, of the seven parameters, there are five parameters with two options and two parameters with three options. The generating unit 21 identifies 288 analysis methods as the analysis method data 12, which is 2×2×2×2×2×3×3.

[0024] The calculation unit 22 calculates the degree of divergence between two pieces of time series data among the plurality of pieces of time series data, using each of the plurality of analysis methods. The degree of divergence is an index of dissimilarity between the two pieces of time series data. In the embodiment of the present invention, the degree of divergence is calculated as a value between 0 and 1. The degree of divergence may be calculated as an arbitrary value and then normalized to a value between 0 and 1. When the time series data group 13 includes 100 types of time series data, the number of combinations of the two pieces of time series data is 100×99 / 2=4950. When the number of analysis methods is 288, the calculation unit 22 calculates the degree of divergence for each of the 4950 combinations, using the 288 analysis methods.

[0025] When the deviations for a combination of two time series data are calculated by each of the multiple analysis methods, the calculation unit 22 calculates an evaluation value of the variation of each deviation calculated by each of the multiple analysis methods for that combination. The calculation unit 22 calculates an evaluation value for each combination of two time series data among the multiple time series data. The calculation unit 22 stores the calculated evaluation value in the evaluation value data 15. When the number of combinations of two time series data is 4950, the calculation unit 22 calculates 4950 evaluation values.

[0026] In an embodiment of the present invention, the evaluation value is calculated so as to have a positive correlation with the closeness of each deviation calculated by each of the multiple analysis methods to the median value, and to have a positive correlation with the variance of each deviation. Here, the median value is the median value between the minimum and maximum deviation values ​​that can be calculated by the analysis methods. In an embodiment of the present invention, the deviation is calculated between 0 and 1, so the median value is 0.5. The evaluation value is calculated, for example, by formula (1).

[0027]

number

[0028] A deviation close to 0 indicates that there is no deviation, i.e., the two time series data are similar, while a deviation close to 1 indicates that there is deviation, i.e., the two time series data are dissimilar. Therefore, a deviation close to the intermediate value indicates that it is difficult to judge the similarity of the two time series data. Also, a large variance value indicates that there is variation in the deviation calculated by each analysis method, making it difficult to judge the similarity of the two time series data.

[0029] The extraction unit 23 extracts a combination of two time series data whose evaluation value satisfies a predetermined condition. The extraction unit 23 extracts a combination of two time series data whose evaluation value is relatively high. Here, a relatively high evaluation value means that the evaluation value is higher than other evaluation values. The extraction unit 23 may extract combinations whose evaluation value is equal to or greater than a predetermined threshold value, or may extract a predetermined number of combinations in descending order of evaluation value. The extraction unit 23 generates extraction data 16 that specifies the extracted combinations.

[0030] There are cases where the evaluator judges that it is possible to determine whether or not there is a discrepancy between the two time series data of the combinations extracted by the extraction unit 23. In this case, the update unit 24 displays the two time series data of the combinations extracted by the extraction unit 23 side by side. The update unit 24 allows the evaluator, who has observed the two time series data of the combinations extracted by the extraction unit 23, to select a combination that is judged to be able to determine whether or not there is a discrepancy between the two time series data. The update unit 24 excludes options that are factors that have caused the evaluation value to be calculated high for the combinations selected by the evaluator.

[0031] The update unit 24 excludes options used in an analysis method that calculated a relatively high deviation for a combination that the evaluator judged to have no deviation from among the options. Here, a relatively high deviation means that the deviation is higher than other deviations among the multiple deviations calculated by each analysis method for a combination that the evaluator judged to have no deviation. The update unit 24 may specify an analysis method that calculated a deviation higher than the threshold value in comparison with a predetermined threshold value as an analysis method that calculates a relatively high deviation. The update unit 24 may specify an analysis method that calculated a predetermined number of deviations in descending order of deviations as an analysis method that calculates a relatively high deviation. The update unit 24 excludes options used in an analysis method that calculates a relatively high deviation from the deviations calculated for a combination that the evaluator judged to have no deviation as options that are the cause of the high evaluation value.

[0032] The update unit 24 excludes, from among the options, options used in an analysis method that calculated a relatively low deviation for a combination that the evaluator judged to have a deviation. Here, a relatively low deviation means that, for a combination that the evaluator judged to have a deviation, the deviation is lower than other deviations among the multiple deviations calculated by each analysis method. The update unit 24 may specify, as an analysis method that calculates a relatively low deviation, an analysis method that calculates a deviation lower than the threshold value in comparison with a predetermined threshold value. The update unit 24 may specify, as an analysis method that calculates a relatively low deviation, an analysis method that calculates a predetermined number of deviations in descending order of deviation. The update unit 24 excludes, from among the deviations calculated for a combination that the evaluator judged to have a deviation, options used in an analysis method that calculates a relatively low deviation as options that are the cause of a high evaluation value.

[0033] The update unit 24 may exclude all options used in the analysis method that calculates a relatively high deviation degree for combinations that the evaluator judges to have no deviation and options used in the analysis method that calculates a relatively low deviation degree for combinations that the evaluator judges to have a deviation, or may exclude only some of the options. For example, the update unit 24 may exclude options that are the cause of a high evaluation value for a predetermined number or more of combinations. Furthermore, the update unit 24 may have the evaluator evaluate each of the combinations of two time-series data whose evaluation values ​​satisfy a predetermined condition and are extracted by the extraction unit 23, or may have the evaluator evaluate some of the combinations.

[0034] When the update unit 24 removes an option, the calculation unit 22 calculates a new evaluation value for each combination of two time series data from the deviations calculated by each of the new multiple analysis methods that selected one of the options after the removal. The calculation unit 22 calculates a new evaluation value for each combination of two time series data from the multiple time series data using the deviations calculated by each analysis method that selected one of the options after the removal, among the deviations calculated by each analysis method that selected one of the options before the removal. The extraction unit 23 extracts a combination of two time series data whose new evaluation value satisfies a predetermined condition. The extraction unit 23 generates extraction data 16 that specifies the extracted combination.

[0035] The elimination of options by the update unit 24 may be repeated multiple times. For example, the update unit 24 may repeat the elimination of options until the evaluator who observed the two time-series data of the combinations extracted by the extraction unit 23 determines that it is not possible to determine the presence or absence of discrepancy between the two time-series data for any of the combinations, or may repeat the elimination a predetermined number of times.

[0036] The output unit 25 outputs the combination extracted by the extraction unit 23. The output unit 25 outputs the extracted data 16 as data that is difficult for a data scientist to judge. When an evaluator who observed the two time series data of the combination extracted by the extraction unit 23 judges that it is impossible to determine whether or not there is a discrepancy between the two time series data, the output unit 25 may output the extracted data 16 as data that is difficult for a data scientist to judge.

[0037] The evaluation value according to the embodiment of the present invention will be described with reference to Fig. 6 and Fig. 7. Five combinations of two time series data will be described in Fig. 6 and Fig. 7. The deviations and evaluation values ​​shown in Fig. 6 are calculated for each combination of time series data shown in Fig. 7. Fig. 6(a) shows the distribution of deviations calculated by each analysis method for each combination. Fig. 6(b) shows the average deviations and evaluation values ​​calculated by each analysis method for each combination.

[0038] As shown in FIG. 6(a), combinations with a deviation degree close to 0, specifically the combination of "rent" and "footwear", are clearly similar, as shown in FIG. 7(d). Combinations with a deviation degree close to 1, specifically the combination of "seafood" and "barber tools", are clearly dissimilar, as shown in FIG. 7(e). In this way, combinations with a deviation degree close to 0 or 1 are clearly similar or dissimilar, are calculated to have a low evaluation value, and are not extracted as extracted data 16.

[0039] On the other hand, combinations with deviations close to 0.5 are calculated to have high evaluation values ​​and are likely to be extracted as extracted data 16. For example, combinations with deviations close to 0.5 include the combination of "carpet" and "cream A" and the combination of "egg" and "ice cream." As shown in FIG. 6(a), the combination of "carpet" and "cream A" has a larger variance of each deviation compared to the combination of "egg" and "ice cream," and therefore is calculated to have a higher evaluation value.

[0040] In an embodiment of the present invention, when the results shown in FIG. 6 are obtained and two combinations are extracted as extracted data 16, the processing device 1 extracts the combination of "Carpet" and "Cosmetic Cream A" and the combination of "Egg" and "Ice Cream" in descending order of evaluation value. It is difficult to determine whether the time series data of "Carpet" and "Cosmetic Cream A" are similar or dissimilar, as shown in FIG. 7(a). It is also difficult to determine whether the time series data of "Egg" and "Ice Cream" are similar or dissimilar, as shown in FIG. 7(b). Therefore, the evaluation value calculated by the processing device 1 can be an index for determining whether the time series data are similar or dissimilar.

[0041] (Processing method) The processing by the processing device 1 according to the embodiment of the present invention will be described with reference to FIGS.

[0042] In step S1, the processing device 1 selects each option from a plurality of options for parameters used in the analysis method, and identifies a plurality of analysis methods.

[0043] The processing device 1 processes steps S2 and S3 for each combination of any two pieces of time series data in the time series data group 13. In step S2, the processing device 1 calculates a plurality of deviations for the two pieces of time series data of the combination to be processed, using each of the plurality of analysis methods identified in step S1. In step S3, the processing device 1 calculates an evaluation value from the variation of the plurality of deviations calculated in step S2.

[0044] After calculating the evaluation value for each combination, the process proceeds to step S4. In step S4, the processing device 1 displays to the evaluator the time series data of the combinations that satisfy the predetermined condition. For example, the processing device 1 displays the time series data of combinations with relatively high evaluation values, such as combinations with evaluation values ​​equal to or greater than a threshold value, or a predetermined number of combinations in descending order of evaluation values.

[0045] In step S5, the processing device 1 assigns processing depending on whether or not there is a combination for which the evaluator can determine the presence or absence of deviation among the combinations of time-series data displayed in step S4. If there is a combination for which the evaluator can determine the presence or absence of deviation, an update process is performed in step S6. In the update process, the option that caused the evaluation value to be calculated high is excluded.

[0046] In step S7, the processing device 1 calculates a new evaluation value from each deviation calculated by a new analysis method obtained by selecting each option after being excluded by the update process. Here, the processing device 1 identifies, among the deviations calculated in step S2, deviations calculated by an analysis method other than the analysis method using the option excluded by the update process, and calculates an evaluation value from the identified deviations. A new evaluation value is calculated for each combination of time-series data.

[0047] When a new evaluation value is calculated, in step S4, the processing device 1 displays the time-series data of the combinations that satisfy the predetermined conditions to the evaluator, and prompts the evaluator to input again whether there is a combination that allows the evaluator to determine whether there is a deviation. If there is a combination that allows the evaluator to determine whether there is a deviation, the processing device 1 processes steps S7 and S8, and returns to step S4. If there is no combination of the time-series data displayed in step S4 that allows the evaluator to determine whether there is a deviation, the processing device 1 proceeds to step S8. In step S8, the processing device 1 outputs combinations with relatively high evaluation values ​​as data that is difficult for data scientists to judge.

[0048] The update process will be described with reference to Fig. 9. First, in step S51, the processor 1 initializes the counter for each option.

[0049] Steps S52 to S54 are processed for each combination of time series data. In step S52, the processing device 1 allocates processing based on the evaluator's judgment for the combination to be processed. If the evaluator judges that there is no deviation for the combination to be processed, the process proceeds to step S53. In step S53, the processing device 1 increments the counter for the option used in the analysis method that calculated a high deviation. If the evaluator judges that there is deviation for the combination to be processed, the process proceeds to step S54. In step S54, the processing device 1 increments the counter for the option used in the analysis method that calculated a low deviation. If the evaluator cannot judge the combination to be processed, the counter for the option is not changed.

[0050] When the processes from step S52 to step S54 are completed for each combination of time series data, the process proceeds to step S55. In step S55, the processing device 1 excludes options whose counter values ​​satisfy a predetermined condition, specifically, options whose counter values ​​are higher than a predetermined threshold value, or a predetermined number of options in descending order of counter values.

[0051] Next, referring to Fig. 10, the evaluation value calculated by the processing device 1 is compared with the subject's judgment of similarity or dissimilarity. The evaluation value calculated by the processing device 1 is divided into five stages, six combinations of two time series data are extracted from each stage, and a total of 30 combinations of time series data are displayed to each subject. The subject evaluates the similarity or dissimilarity of the two displayed time series data for each combination. The subject gives one point if the two time series data are similar, and seven points if they are dissimilar, rating them on a seven-stage scale according to the degree of similarity or dissimilarity.

[0052] FIG. 10 shows the evaluation value calculated by the processing device 1 for a certain set of time-series data in association with the variance of the evaluations by six subjects. The vertical axis of FIG. 10 is the variance of the evaluations by six subjects for one combination of time-series data. The horizontal axis is the evaluation value calculated by the processing device 1. The evaluation value on the horizontal axis is scaled to range from approximately 0 to 100, with the weights m and n in formula (1) each set to 2.

[0053] 10, it can be seen that the variance of the subjects' answers increases as the evaluation value increases. This shows that the evaluation value calculated by the processing device 1 represents the difficulty of determining whether two time-series data are similar or dissimilar.

[0054] In this way, the processing device 1 according to the embodiment of the present invention can calculate an evaluation value indicating the difficulty of determining whether two pieces of time-series data are similar or dissimilar. The processing device 1 can support data scientists in improving the efficiency of data analysis by extracting combinations of time-series data that are difficult for data scientists to determine from a large amount of data based on the evaluation value.

[0055] The processing device 1 of the present embodiment described above is, for example, a general-purpose computer system including a CPU (Central Processing Unit, processor) 901, a memory 902, a storage 903 (HDD: Hard Disk Drive, SSD: Solid State Drive), a communication device 904, an input device 905, and an output device 906. In this computer system, the CPU 901 executes a program loaded on the memory 902, thereby realizing each function of the processing device 1.

[0056] The processing device 1 may be implemented in one computer or in multiple computers, and may also be a virtual machine implemented in a computer.

[0057] The program of the processing device 1 can be stored in a computer-readable recording medium such as an HDD, SSD, USB (Universal Serial Bus) memory, CD (Compact Disc), or DVD (Digital Versatile Disc), or can be distributed via a network.

[0058] The present invention is not limited to the above-described embodiment, and various modifications are possible within the scope of the present invention. [Explanation of symbols]

[0059] 1 Processing equipment 11 Parameter Data 12. Analysis Method Data 13 Time series data set 14 Deviation Data 15 Evaluation value data 16 Extracted Data 21 Generation part 22 Calculation section 23 Extraction part 24 Update section 25 Output section 901 CPU 902 Memory 903 Storage 904 Communication equipment 905 Input Device 906 Output Device

Claims

1. Calculating the degree of discrepancy between two of the plurality of time series data using each of the plurality of analysis methods; a calculation unit that calculates an evaluation value of the variation of each deviation degree calculated by each of the plurality of analysis methods for each combination of two time series data among a plurality of time series data; an extraction unit that extracts a combination of two pieces of time-series data whose evaluation value satisfies a predetermined condition; An output section that outputs the extracted combinations A processing device comprising:

2. a generation unit that generates, as the plurality of analysis methods, each analysis method in which one of the parameter options for calculating the degree of discrepancy between two pieces of time series data is selected; The processing device of claim 1 further comprising:

3. an update unit that has an evaluator who has observed two time-series data of the combinations extracted by the extraction unit select a combination that is determined to be capable of determining the presence or absence of a discrepancy between the two time-series data, and excludes, from among the options, an option that has been used in an analysis method that has calculated the degree of discrepancy relatively high for a combination that the evaluator has determined to have no discrepancy, and an option that has been used in an analysis method that has calculated the degree of discrepancy relatively low for a combination that the evaluator has determined to have a discrepancy; The calculation unit calculates a new evaluation value from each deviation calculated by each of a plurality of new analysis methods in which one of the options after the exclusion is selected for each combination of two time series data among the plurality of time series data; The extraction unit extracts a combination of two pieces of time-series data for which the new evaluation value satisfies a predetermined condition. The processing device according to claim 2 .

4. The output unit outputs the combination extracted by the extraction unit when an evaluator who has observed the two time series data of the combination extracted by the extraction unit determines that it is impossible to determine whether or not there is a discrepancy between the two time series data. The processing device of claim 1 .

5. The evaluation value has a positive correlation with the closeness of each deviation degree calculated by each of the plurality of analysis methods to the median value, and has a positive correlation with the variance of each deviation degree. The processing device of claim 1 .

6. The evaluation value is calculated by the formula (1). The processing device of claim 1 . [0010]

7. The computer calculates a degree of discrepancy between two pieces of time series data among the plurality of pieces of time series data by each of the plurality of analysis methods; the computer calculates an evaluation value of the variation of each deviation degree calculated by each of the plurality of analysis methods for each combination of two time series data among the plurality of time series data; The computer extracts a combination of two pieces of time-series data whose evaluation value satisfies a predetermined condition, The computer outputs the extracted combinations. Processing methods.

8. A program for causing a computer to function as the processing device according to any one of claims 1 to 6.

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