Time-series data analysis system and method

Abstract

The present invention relates to a time-series data analysis system and method for analyzing time-series data to detect abnormality, etc. and provides a time-series data analysis system and an operation method thereof, the system comprising: a data reception unit for receiving time-series data for multiple variables; a data processing unit for generating a feature map by using the received time-series data and combined time-series data generated by combining two or more pieces of time-series data: a data learning unit for generating a time-series data analysis model by using multiple feature maps generated on the basis of multiple time points; and a data analysis unit for analyzing time-series data by using the generated time-series data analysis model.

Description

Time series data analysis system and method

[0001] The present invention relates to a time series data analysis system and method, and more specifically, to a system and method for creating an artificial intelligence model that learns time series data of various variables to analyze time series data, and using the same to analyze time series data.

[0002] As artificial intelligence technology develops, technologies that analyze various time-series monitoring data received to analyze industrial environments and detect or predict abnormal situations are being widely used.

[0003] In particular, the convolutional neural network (CNN) is a structure that extracts features from data and identifies patterns in the features, and has the advantage of being able to quickly analyze large-scale multivariate time series data, so it is being used to analyze multivariate time series data.

[0004] The prior art, Korean Patent Publication No. 10-2023-0033312, "CNN-based multivariate data processing system and CNN-based multivariate data processing method", is a technology that enables monitoring and analyzing multivariate time series data using such a synthetic neural network. However, in such prior arts, there was a problem that it was difficult to consider the characteristics due to the correlation between each variable by extracting and analyzing features from each multivariate time series variable.

[0005] Therefore, a time series data analysis method that considers the correlation between variables, rather than simply analyzing the characteristics of each variable, is required.

[0006] The purpose of the present invention is to provide analysis results by analyzing time series data of various variables.

[0007] The purpose of the present invention is to enable analysis of time series data by considering characteristics resulting from correlations between various variables, rather than by considering only the individual characteristics of various variables.

[0008] The purpose of the present invention is to enable rapid analysis results to be derived while considering both the features derived from the flow according to the time progression of time series data and the features derived from the correlation between variables.

[0009] In order to achieve this purpose, a time series data analysis system according to an embodiment of the present invention may be configured to include a data receiving unit that receives time series data for a plurality of variables, a data processing unit that generates a characteristic map using the received time series data and combined time series data generated by combining two or more time series data, a data learning unit that generates a time series data analysis model using a plurality of characteristic maps generated based on a plurality of time points, and a data analysis unit that analyzes the time series data using the generated time series data analysis model.

[0010] At this time, the data processing unit may be configured to generate an original one-dimensional matrix by configuring time series data of the past n points in time for each of the plurality of variables as an n * 1 matrix based on each point in time, generate a combined one-dimensional matrix by combining two or more of the generated original one-dimensional matrices to generate an n * 1 matrix for each combination, and generate the feature map using the original one-dimensional matrix and the combined one-dimensional matrix.

[0011] In addition, if the number of the plurality of variables is m, the data processing unit can generate a combination one-dimensional matrix for each of all cases of combining the generated original one-dimensional matrix from 2 to m.

[0012] In addition, the data processing unit may be configured to generate a combined two-dimensional matrix by accumulating original one-dimensional matrices to be combined, and input the generated combined two-dimensional matrix into a 1*k convolution layer (k is the number of original one-dimensional matrices to be combined) to generate a combined one-dimensional matrix.

[0013] At this time, the data processing unit may be configured to generate the feature map by combining the data generated by applying a 1*1 convolution layer to the generated original 1-dimensional matrix and the combined 1-dimensional matrix.

[0014] The present invention can obtain the effect of providing analysis results by analyzing time series data of various variables.

[0015] The present invention can achieve the effect of enabling analysis of time series data by considering not only the individual characteristics of various variables, but also the characteristics resulting from correlations between various variables.

[0016] The present invention can obtain the effect of quickly deriving analysis results while considering both the features derived from the flow according to the time progression of time series data and the features derived from the correlation between variables.

[0017] FIG. 1 is a diagram illustrating the internal configuration of a time series data analysis system according to one embodiment of the present invention.

[0018] FIG. 2 is a diagram illustrating an example of time series data input into a time series data analysis system according to one embodiment of the present invention.

[0019] FIG. 3 is a diagram illustrating an example of extracting feature data from time series data in a time series data analysis system according to an embodiment of the present invention.

[0020] FIG. 4 is a diagram showing an example of a feature extraction module used in a time series data analysis system according to one embodiment of the present invention.

[0021] FIG. 5 is a diagram showing an example of an inception module used in a time series data analysis system according to one embodiment of the present invention.

[0022] FIG. 6 is a diagram showing a neural network structure used in a time series data analysis system according to an embodiment of the present invention.

[0023] Figure 7 is a flowchart showing the flow of a time series data analysis method according to one embodiment of the present invention.

[0024] In order to achieve this purpose, a time series data analysis system according to an embodiment of the present invention may be configured to include a data receiving unit that receives time series data for a plurality of variables, a data processing unit that generates a characteristic map using the received time series data and combined time series data generated by combining two or more time series data, a data learning unit that generates a time series data analysis model using a plurality of characteristic maps generated based on a plurality of time points, and a data analysis unit that analyzes the time series data using the generated time series data analysis model.

[0025] At this time, the data processing unit may be configured to generate an original one-dimensional matrix by configuring time series data of the past n points in time for each of the plurality of variables as an n * 1 matrix based on each point in time, generate a combined one-dimensional matrix by combining two or more of the generated original one-dimensional matrices to generate an n * 1 matrix for each combination, and generate the feature map using the original one-dimensional matrix and the combined one-dimensional matrix.

[0026] In addition, if the number of the plurality of variables is m, the data processing unit can generate a combination one-dimensional matrix for each of all cases of combining the generated original one-dimensional matrix from 2 to m.

[0027] In addition, the data processing unit may be configured to generate a combined two-dimensional matrix by accumulating original one-dimensional matrices to be combined, and input the generated combined two-dimensional matrix into a 1*k convolution layer (k is the number of original one-dimensional matrices to be combined) to generate a combined one-dimensional matrix.

[0028] At this time, the data processing unit may be configured to generate the feature map by combining the data generated by applying a 1*1 convolution layer to the generated original 1-dimensional matrix and the combined 1-dimensional matrix.

[0029] Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the attached drawings. In describing the present invention, detailed descriptions of known components or functions will be omitted if they are deemed to obscure the gist of the invention. Furthermore, specific numerical values ​​used in describing embodiments of the present invention are merely exemplary and do not limit the scope of the invention.

[0030] The time-series data analysis system according to the present invention may be configured as a server equipped with a central processing unit (CPU) and memory, and capable of connecting to other terminals via a communication network such as the Internet. However, the present invention is not limited to the configuration of the central processing unit and memory. Furthermore, the time-series data analysis system according to the present invention may be physically configured as a single device, or may be implemented in a distributed manner across multiple devices.

[0031] FIG. 1 is a diagram illustrating the internal configuration of a time series data analysis system according to one embodiment of the present invention.

[0032] As illustrated in the drawing, a time series data analysis system (101) according to one embodiment of the present invention may be configured to include a data receiving unit (110), a data processing unit (120), a data learning unit (130), and a data analysis unit (140).

[0033] Each component may be a software module that operates within the same physical computer system, and may be configured so that two or more physically separated computer systems can operate in conjunction with each other, and various embodiments that include the same function fall within the scope of the present invention.

[0034] The data receiving unit (110) receives time-series data for multiple variables. Time-series data is continuously generated and collected over time and may include a variety of information for monitoring industrial sites or equipment, such as factories. For example, status information, such as temperature and humidity, of a specific space may be continuously collected, and information, such as voltage and current, of power supplied to specific equipment may also be time-series data.

[0035] The time series data received from the data receiving unit (110) may be data measured at multiple points in time. For example, if voltage and current information is measured and collected at 1-second intervals, the data may be time series data at each point in time at 1-second intervals.

[0036] Time series data received from the data receiving unit (110) can be synchronized between data points for analysis. If there is a difference in the time points measured by each type of data, data measured at the same time can be extracted to match the time points, or data can be corrected for some data to correct the data so that it is in the same form as if it were measured at the same time point, thereby achieving synchronization.

[0037] The data processing unit (120) generates a feature map using the received time series data and combined time series data generated by combining two or more time series data. The feature map refers to data for analysis created by deriving features from data to enable rapid analysis in an artificial intelligence model such as a convolutional neural network (CNN). In general, a convolutional neural network uses a number of convolutional layers that derive features through filters to extract feature information that can be used for actual abnormal state detection, prediction, etc., and by learning using the feature information, a necessary neural network model can be derived.

[0038] Typically, when analyzing multivariate time series data, feature maps are derived from each variable and then synthesized to create data for analysis. However, during the feature derivation process, features due to correlations between variables may be removed, which can lead to a problem of reduced analysis accuracy.

[0039] Accordingly, the data processing unit (120) generates an original one-dimensional matrix by organizing time series data of the past n points in time for each of the plurality of variables into an n * 1 matrix based on each point in time, and generates a combined one-dimensional matrix by combining two or more of the generated original one-dimensional matrices to configure an n * 1 matrix for each combination, and generates the feature map using the original one-dimensional matrix and the combined one-dimensional matrix. Through this, rather than simply extracting the feature map of the time series data for each variable, it extracts the feature map from the combination of two or more variables, so that it is possible to analyze even the features derived from the correlation between the variables.

[0040] At this time, if the number of the plurality of variables is m, the data processing unit (120) generates a combination 1-dimensional matrix for each of all cases of combining the generated original 1-dimensional matrix from 2 to m. Through this, one 1-dimensional matrix is ​​generated for each variable and variable combination set, and these are accumulated to derive the final feature map.

[0041] For example, if there are 5 variables in total, 5 original 1-dimensional matrices each composed of one variable, 10 combined 1-dimensional matrices generated with 2 variable combinations each, 10 combined 1-dimensional matrices generated with 3 variable combinations each, 5 combined 1-dimensional matrices generated with 4 variable combinations each, and 1 combined 1-dimensional matrix generated with 5 variable combinations are generated, resulting in a total of 31 1-dimensional matrices. If these are accumulated, a 2-dimensional matrix of n * 31 is derived as a feature map, and as many feature maps as the number of channels can be derived.

[0042] The data processing unit (120) accumulates the original one-dimensional matrices to be combined to generate a combined two-dimensional matrix, and inputs the generated combined two-dimensional matrix into a 1*k convolution layer (k is the number of original one-dimensional matrices to be combined) to generate a combined one-dimensional matrix. When combining two or more variables, the original one-dimensional matrices for each variable are accumulated to generate a two-dimensional matrix. Since the original one-dimensional matrix is ​​an n*1 matrix, when combining two variables, an n*2 two-dimensional matrix is ​​generated, and when combining three variables, an n*3 two-dimensional matrix is ​​generated.

[0043] Therefore, when a 1 * 2 convolution layer is applied to an n * 2 2-dimensional matrix created by combining two variables, an n * 1 1-dimensional matrix is ​​derived again. Also, for an n * 3 2-dimensional matrix created by combining three variables, a 1 * 3 convolution layer is applied to derive an n * 1 1-dimensional matrix.

[0044] In addition, the data processing unit (120) generates the feature map by combining the data generated by applying a 1*1 convolution layer to the original 1-dimensional matrix and the combined 1-dimensional matrix. Through this, it is possible to confirm the features according to the time series flow of each variable in the data obtained by applying a 1*1 convolution layer to the original 1-dimensional matrix, and it is possible to confirm the features derived by the correlation between variables through the combined 1-dimensional matrix data derived by the combination of variables.

[0045] The data learning unit (130) creates a time series data analysis model using multiple feature maps generated based on multiple time points. By extracting continuously input time series data at n time points and generating feature maps, multiple feature maps can be generated. For each feature map derived time point, tagging, such as occurrence of an abnormal condition, is performed to derive learning data composed of multiple feature maps, and by learning the feature map, a time series data analysis model that derives analysis results for time series data is created.

[0046] The data analysis unit (140) analyzes time series data using the generated time series data analysis model. By performing training using tagged learning data for multiple feature maps derived from past time series data, a time series data analysis model capable of detecting and predicting abnormal situations for newly input time series data is derived. Afterwards, by dividing real-time input time series data into n time points equally and inputting them, analysis results for the current status can be derived.

[0047] FIG. 2 is a diagram illustrating an example of time series data input into a time series data analysis system according to one embodiment of the present invention.

[0048] As shown in the drawing, time series data is data that is continuously input at specific time intervals. Therefore, in order to analyze the situation at a specific time, it is necessary to extract a set of data from a set number of consecutive time points.

[0049] The drawing shows a case where input data for a total of 25 variables are input in a time series manner. By applying the same time point criterion, n consecutive time point data are derived for all 25 variables, and this is applied to the time series data analysis system of the present invention for analysis.

[0050] n, which corresponds to the size of the window in the drawing, can be set differently depending on the characteristics of the time series data, and an appropriate size can be selected to sufficiently analyze the causal relationship according to the time series flow.

[0051] By constructing a data set using n past data each time a point in time passes by 1, it is possible to construct the data in each data set so that the data overlaps, and it is also possible to construct the next data set with n data after the extracted n data. This data extraction method can be constructed differently depending on the form of the data, the results to be analyzed, etc., and the present invention is not limited thereby.

[0052] FIG. 3 is a diagram illustrating an example of extracting feature data from time series data in a time series data analysis system according to an embodiment of the present invention.

[0053] The figure shows the process of deriving n * 31 feature maps using a time series data set consisting of n time series points for 5 variables.

[0054] As explained above, when a 1-dimensional matrix of n * 1 is input for each of the five variables a, b, c, d, and e, a 1 * 1 convolution layer is applied as shown at the top to derive five n * 1 matrices.

[0055] Afterwards, through combinations of two variables each, an n * 2 matrix is ​​derived for 10 combinations of (a, b), (a, c), (a, d), (a, e), (b, c), (b, d), (b, e), (c, d), (c, e), (d, e), and an n * 2 matrix is ​​derived for each combination by applying a 1 * 2 convolution layer.

[0056] In addition, through combinations of 3 variables each, an n * 3 matrix is ​​derived for 10 combinations of (a, b, c), (a, b, d), (a, b, e), (a, c, d), (a, c, e), (a, d, e), (b, c, d), (b, c, e), (b, d, e), (c, d, e), and an n * 3 matrix is ​​derived for each combination by applying a 1 * 3 convolution layer.

[0057] In addition, through combinations of 4 variables each, an n * 4 matrix is ​​derived for 5 combinations of (a, b, c, d), (a, b, c, e), (a, b, d, e), (a, c, d, e), (b, c, d, e), and an n * 4 matrix is ​​derived for each combination by applying a 1 * 4 convolution layer.

[0058] Finally, a 1 * 5 convolution layer is applied to the 1 * 5 matrix that combines all of (a, b, c, d, e) to derive one n * 1 matrix.

[0059] Through this, a total of 31 n * 1 matrices are derived, and these are accumulated to derive an n * 31 matrix, which is then used as a feature map.

[0060] Depending on the number of channels in the convolutional layer, the number of n * 31 feature maps derived from one data set may vary.

[0061] FIG. 4, FIG. 5 and FIG. 6 are diagrams showing examples of a feature extraction module, an inception module and a neural network structure using the same used in a time series data analysis system according to one embodiment of the invention.

[0062] The time series data analysis system of the present invention can use a network based on a convolutional neural network (CNN), and can utilize a feature extraction module and an inception module configured as shown in FIGS. 4 and 5, and can apply these to configure a neural network structure as shown in FIG. 6.

[0063] Such a structure is only one embodiment for applying the present invention, and the present invention is not limited by such a neural network structure.

[0064] Figure 7 is a flowchart showing the flow of a time series data analysis method according to one embodiment of the present invention.

[0065] The time series data analysis method according to one embodiment of the present invention is a method that operates in a time series data analysis system (101) equipped with a central processing unit (CPU) and memory, and the description of the time series data analysis system (101) described above can be applied as is. Therefore, even if there is no separate description below, it is self-evident that all contents described to explain the time series data analysis system can be applied as is to implementing the time series data analysis method.

[0066] The data reception step (S701) receives time-series data for multiple variables. Time-series data is continuously generated and collected over time and can include a variety of information for monitoring industrial sites or equipment, such as factories. For example, status information, such as temperature and humidity, of a specific space may be continuously collected, or information, such as voltage and current, of power supplied to specific equipment may be time-series data.

[0067] The time series data received in the data receiving step (S701) may be data measured at multiple points in time. For example, if voltage and current information is measured and collected at 1-second intervals, the data may be time series data at each point in time at 1-second intervals.

[0068] In the data reception step (S701), time series data received can be synchronized for analysis. In cases where there is a difference in the time at which data is measured depending on the type of data, data measured at the same time can be extracted to match the time, or data can be corrected for some data to correct the data so that it is in the same form as if it were measured at the same time, thereby achieving synchronization.

[0069] The data processing step (S702) generates a feature map using the received time series data and combined time series data generated by combining two or more time series data. The feature map refers to data for analysis created by extracting features from data to enable rapid analysis in an artificial intelligence model such as a convolutional neural network (CNN). In general, a convolutional neural network uses multiple convolutional layers that extract features through filters to extract feature information that can be used for actual abnormal condition detection and prediction, and by learning using this, it is possible to derive the necessary neural network model.

[0070] Typically, when analyzing multivariate time series data, feature maps are derived from each variable and then synthesized to create data for analysis. However, during the feature derivation process, features due to correlations between variables may be removed, which can lead to a problem of reduced analysis accuracy.

[0071] Accordingly, the data processing step (S702) generates an original one-dimensional matrix by organizing time series data of the past n points in time for each of the plurality of variables into an n * 1 matrix based on each point in time, and generates a combined one-dimensional matrix by combining two or more of the generated original one-dimensional matrices to configure an n * 1 matrix for each combination, and generates the feature map using the original one-dimensional matrix and the combined one-dimensional matrix. Through this, rather than simply extracting the feature map of the time series data for each variable, it extracts the feature map from the combination of two or more variables, so that it is possible to analyze even the features derived from the correlation between the variables.

[0072] At this time, the data processing step (S702) generates a combination 1-dimensional matrix for each of all cases of combining the generated original 1-dimensional matrix from 2 to m, assuming that the number of the above-mentioned multiple variables is m. Through this, one 1-dimensional matrix is ​​generated for each variable and variable combination set, and these are accumulated to derive the final feature map.

[0073] For example, if there are 5 variables in total, 5 original 1-dimensional matrices each composed of one variable, 10 combined 1-dimensional matrices generated with 2 variable combinations each, 10 combined 1-dimensional matrices generated with 3 variable combinations each, 5 combined 1-dimensional matrices generated with 4 variable combinations each, and 1 combined 1-dimensional matrix generated with 5 variable combinations are generated, resulting in a total of 31 1-dimensional matrices. If these are accumulated, a 2-dimensional matrix of n * 31 is derived as a feature map, and as many feature maps as the number of channels can be derived.

[0074] The data processing step (S702) generates a combined two-dimensional matrix by accumulating the original one-dimensional matrices to be combined, and inputs the generated combined two-dimensional matrix into a 1*k convolution layer (k is the number of original one-dimensional matrices to be combined) to generate a combined one-dimensional matrix. When combining two or more variables, the original one-dimensional matrices for each variable are accumulated to generate a two-dimensional matrix. Since the original one-dimensional matrix is ​​an n*1 matrix, when combining two variables, an n*2 two-dimensional matrix is ​​generated, and when combining three variables, an n*3 two-dimensional matrix is ​​generated.

[0075] Therefore, when a 1 * 2 convolution layer is applied to an n * 2 2-dimensional matrix created by combining two variables, an n * 1 1-dimensional matrix is ​​derived again. Also, for an n * 3 2-dimensional matrix created by combining three variables, a 1 * 3 convolution layer is applied to derive an n * 1 1-dimensional matrix.

[0076] In addition, the data processing step (S702) generates the feature map by combining the data generated by applying a 1*1 convolution layer to the original 1-dimensional matrix and the combined 1-dimensional matrix. Through this, it is possible to confirm the characteristics according to the time series flow of each variable in the data obtained by applying a 1*1 convolution layer to the original 1-dimensional matrix, and it is possible to confirm the characteristics derived by the correlation between variables through the combined 1-dimensional matrix data derived from the combination of variables.

[0077] The data learning step (S703) creates a time series data analysis model using multiple feature maps generated based on multiple time points. By extracting continuously input time series data at n time points and generating feature maps, multiple feature maps can be generated. For each feature map derived time point, tags such as abnormality occurrences are performed to derive learning data consisting of multiple feature maps. By learning this data, a time series data analysis model that derives analysis results for time series data is created.

[0078] The data analysis step (S704) analyzes time series data using the generated time series data analysis model. By performing training using tagged learning data for multiple feature maps derived from past time series data, a time series data analysis model capable of detecting and predicting abnormal situations for newly input time series data is derived. Then, by dividing real-time input time series data into n time points and inputting them, analysis results for the current state can be derived.

[0079] The time series data analysis method according to the present invention can be produced as a program for causing a computer to execute the program and recorded on a computer-readable recording medium.

[0080] Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks, and magnetic tape, optical recording media such as CDROMs and DVDs, magneto-optical media such as floptical disks, and hardware devices specifically configured to store and execute program instructions such as ROM, RAM, and flash memory.

[0081] Examples of program instructions include not only machine language codes, such as those generated by a compiler, but also high-level language codes that can be executed by a computer using an interpreter or the like. The hardware device may be configured to operate as one or more software modules to perform processing according to the present invention, and vice versa.

[0082] Although the present invention has been described above with reference to embodiments, those skilled in the art can modify and change the present invention in various ways without departing from the spirit and scope of the present invention as set forth in the following claims.

[0083] The present invention relates to a time series data analysis system and method for analyzing time series data to detect abnormalities, etc., and provides a time series data analysis system and an operation method thereof, including a data receiving unit for receiving time series data for a plurality of variables, a data processing unit for generating a characteristic map using the received time series data and combined time series data generated by combining two or more time series data, a data learning unit for generating a time series data analysis model using a plurality of characteristic maps generated based on a plurality of time points, and a data analysis unit for analyzing the time series data using the generated time series data analysis model.

Claims

1. A data receiving unit that receives time series data for multiple variables; A data processing unit that creates a feature map using the received time series data and combined time series data created by combining two or more time series data; A data learning unit that creates a time series data analysis model using multiple feature maps generated based on multiple time points, and A data analysis unit that analyzes time series data using the above-generated time series data analysis model. Time series data analysis system.

2. In paragraph 1, The above data processing unit Based on each point in time, the time series data of the past n points in time are organized into an n * 1 matrix for each of the above multiple variables to create an original one-dimensional matrix. By combining two or more of the above-generated original one-dimensional matrices and constructing an n * 1 matrix for each combination, a combined one-dimensional matrix is ​​generated, Generating the feature map using the original one-dimensional matrix and the combined one-dimensional matrix A time series data analysis system featuring .

3. In paragraph 2, The above data processing unit If the number of the above multiple variables is m, Generating a combination 1-dimensional matrix for each of all cases of combining the original 1-dimensional matrix generated above from 2 to m. A time series data analysis system featuring .

4. In paragraph 3, The above data processing unit Accumulate the original one-dimensional matrix to be combined to create a combined two-dimensional matrix, Generating a combined 1-dimensional matrix by inputting the above-generated combined 2-dimensional matrix into a 1*k convolution layer (k is the number of original 1-dimensional matrices to be combined). A time series data analysis system featuring .

5. In paragraph 4, The above data processing unit Generating the feature map by combining the data generated by applying a 1*1 convolution layer to the original 1-dimensional matrix generated above and the combined 1-dimensional matrix A time series data analysis system featuring .

6. A time series data analysis method operating in a time series data analysis system equipped with a central processing unit and memory, A data receiving step for receiving time series data for multiple variables; A data processing step of creating a feature map using the received time series data and combined time series data created by combining two or more time series data; A data learning step for creating a time series data analysis model using multiple feature maps generated based on multiple time points; and A data analysis step for analyzing time series data using the above-generated time series data analysis model. Methods for analyzing time series data.

7. In paragraph 6, The above data processing steps are Based on each point in time, the time series data of the past n points in time are organized into an n * 1 matrix for each of the above multiple variables to create an original one-dimensional matrix. By combining two or more of the above-generated original one-dimensional matrices and constructing an n * 1 matrix for each combination, a combined one-dimensional matrix is ​​generated, Generating the feature map using the original one-dimensional matrix and the combined one-dimensional matrix A time series data analysis method characterized by .

8. In paragraph 7, The above data processing steps are If the number of the above multiple variables is m, Generating a combination 1-dimensional matrix for each of all cases of combining the original 1-dimensional matrix generated above from 2 to m. A time series data analysis method characterized by .

9. In paragraph 8, The above data processing steps are Accumulate the original one-dimensional matrix to be combined to create a combined two-dimensional matrix, Generating a combined 1-dimensional matrix by inputting the above-generated combined 2-dimensional matrix into a 1*k convolution layer (k is the number of original 1-dimensional matrices to be combined). A time series data analysis method characterized by .

10. In paragraph 9, The above data processing steps are Generating the feature map by combining the data generated by applying a 1*1 convolution layer to the original 1-dimensional matrix generated above and the combined 1-dimensional matrix A time series data analysis method characterized by .

11. A computer-readable recording medium having recorded thereon a program for causing a computer to execute the method of any one of clauses 6 to 10.

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