KR20170078256A - Method and apparatus for time series data prediction - Google Patents

Abstract

A time series data prediction method is provided. The method of predicting time series data according to an embodiment of the present invention includes the steps of i) clustering measurement time series data of a predetermined period unit during a training period into a plurality of clusters, ii) collecting a plurality of environment data during the training period (Iii) selecting at least a part of the plurality of environmental data as a factor, iv) selecting a classification model that optimally classifies clusters of the measured time series data on a space or a plane constituted by axes indicating the factors Determining a performance indicator value of the generated classification model, selecting the factor as the factor, generating the classification model, and determining the performance indicator value by changing the selection of the factor The optimum classification model among the generated classification models based on the performance index value Step, v) selecting for and a step of using the optimum classification model, predicting the measurements of the cluster series data of the forecast period.

Description

TECHNICAL FIELD The present invention relates to a method and apparatus for predicting time series data,

The present invention relates to a method and apparatus for predicting time series data. More particularly, the present invention relates to a method and apparatus for predicting time series data for a specific period by using a result of training time series data generated during a certain period of the past.

Time series data refers to data expressed as a function of time for a certain period of time. Such time series data can be predicted through analysis of past time series data.

In the case of predicting the time series data, time series data generated in the past training period is clustered with time series data of similar patterns, and a cluster corresponding to the prediction period is determined, so that the time series data of the prediction period has a pattern similar to the determined cluster Lt; RTI ID = 0.0 > a < / RTI >

At this time, the criterion for determining the cluster corresponding to the prediction period is determined depending on the expert's experience or knowledge. For example, when estimating the power consumption (24 hours basis) of a building, a power consumption cluster corresponding to a forecast period may be corresponded to a day type of weekday / Saturday / holiday. In the above example, the pattern of the power consumption amount can be changed on the basis of the type of date is obtained depending on knowledge or experience.

The existing method of predicting the time series data depends on the knowledge or experience of the expert and the criterion for determining the clusters of the target time series data is selected. In addition, since it is dependent on people, it is also difficult to establish clear criteria for determining clusters of target time series data based on environmental time series data such as temperature and humidity indicating the environment of the forecast period.

Korean Patent Publication No. 1998-7002852 Korean Patent Publication No. 2009-0073937

SUMMARY OF THE INVENTION The present invention provides a method of automatically generating an optimal reference model for determining a cluster of target time series data and determining a cluster of the target time series data in the prediction period by using the optimum reference model And a device therefor.

Another object of the present invention is to provide a method of using environment time series data indicating an environment that may affect the target time series data as a factor of a reference model for determining a cluster of target time series data, Device.

According to another aspect of the present invention, there is provided a computer-readable storage medium storing a program for causing a computer to function as a reference model for determining a cluster of target time series data, And a method of using the same as a factor.

It is another object of the present invention to provide a method and apparatus for predicting target time series data of a prediction period with high accuracy by using a prediction model constructed for each cluster of target time series data.

The technical objects of the present invention are not limited to the above-mentioned technical problems, and other technical subjects not mentioned can be clearly understood by those skilled in the art from the following description.

According to another aspect of the present invention, there is provided a method of predicting time series data, comprising the steps of: i) clustering measurement time series data of a predetermined period unit during a training period into a plurality of clusters; ii) Collecting a plurality of environmental data, iii) selecting at least a part of the plurality of environmental data as a factor, iv) clustering the measurement time series data in a space or a plane constituted by axes indicating the factor Determining a performance index value of the generated classification model, selecting the factor as the factor, generating the classification model, and determining the performance indicator value, And repeating the selection while changing the selection of the factor, Selecting an optimal classification model among the generated classification models, and v) predicting clusters of the measurement time series data in the prediction period using the optimal classification model.

In one embodiment, the collecting of the environmental data includes a step of clustering environmental time series data of a predetermined period unit of the collected environment data into a plurality of clusters. In this case, when the environment time series data among the selected factors is included, the axis indicating the environmental time series data is an axis indicating the cluster of the environmental time series data.

In one embodiment, generating the classification model comprises generating the classification model using Support Vector Machine logic, wherein the determining the performance index comprises: generating a classification model using a hyperplane < RTI ID = 0.0 > and using a maximum margin according to a hyperplane as a performance index of the classification model.

In one embodiment, the step of predicting the clusters of the time series data of the predicted time period comprises the steps of: receiving a predicted value of the environmental data used as a factor of the selected classification model among the environmental data of the prediction period; And inputting the predicted value of the environmental data into the optimal classification model to predict the cluster of the measured time series data.

In one embodiment, clustering the measured time series data into a plurality of clusters comprises clustering each of the measured time series data into k clusters using k-means logic, Barycenter Averaging) logic to generate representative time series data for each cluster.

According to another aspect of the present invention, there is provided a method for predicting time series data according to an embodiment of the present invention, comprising the steps of: i) Predicting the measured time series data of the prediction period by using a regression model for the cluster of the selected measurement time series data. The regression model may include a first model and a second model, wherein the regression model includes first environment time series data of the environment data as a first independent variable and the measurement value time series data as a dependent variable, Wherein the step of predicting the measured time series data includes using the first model when the cluster of the measured time series data is the first cluster and using the first model when the cluster of the measured time series data is the second cluster different from the first cluster, And using a second model different from the first model.

Wherein the regression model has at least one of a cluster identifier of second environment time series data different from the first environment time series data and a representative value representing a specific environment of each cycle of the environment data in addition to the first independent variable, .

In one embodiment, the time-series data prediction method includes: clustering measurement time series data of a predetermined period unit during the training period into a plurality of clusters; collecting a plurality of different types of environment data during the training period; And constructing a regression model corresponding to a cluster of each measurement time series data. In this case, the step of constructing the regression model may include using only the data of the period clustered in the first measurement time series data cluster without using the data of the period clustered in the second measurement value time series data cluster, And constructing a regression model corresponding to the cluster.

According to an aspect of the present invention, there is provided an apparatus for predicting time series data, the apparatus comprising: a memory for loading a computer program for analyzing measurement time series data during a training period to predict the measurement time series data in a prediction period; A processor for executing the computer program loaded in the memory; a network interface; and storage for storing measurement time series data received via the network interface, the environment data, and data inquired by the computer program. Wherein the computer program includes training logic and prediction logic, the training logic comprising: i) clustering measurement time series data of a predetermined period unit during a training period into a plurality of clusters, ii) (Iii) an operation of selecting at least a part of the plurality of environmental data by a factor, iv) a cluster of the measured time series data in a space or a plane constituted by axes indicating the factor, V) an operation to determine a performance index value of the generated classification model, vi) a step of selecting as the factor, a step of generating the classification model, and a step of calculating the performance index value Determining a step of changing the selection of the factor, And includes an operation of selecting the best classification model of the generated classification model, based on the parameter values. Further, the prediction logic includes an operation of using the optimal classification model to predict a cluster of the measured time series data in the prediction period.

In one embodiment, the prediction logic further comprises an operation of predicting the measured time series data of the prediction period using a regression model for a cluster of the predicted measured time series data.

1 is a block diagram illustrating a time-series data prediction system according to an embodiment of the present invention.
2 to 4 are flowcharts of a time series data predicting method according to an embodiment of the present invention.
5 is a view for explaining measured time series data referred to in some embodiments of the present invention.
FIG. 6 is a diagram for explaining a result of clustering measurement time series data of FIG. 5 and generating representative time series data of each cluster; FIG.
FIG. 7 is a view for explaining a storage form of a result of performing clustering on measured time series data collected during a training period in some embodiments of the present invention. FIG.
8 is a diagram for explaining multidimensional measurement time series data referred to in some embodiments of the present invention.
9 is a diagram for explaining a process of determining the number of clusters when clustering time series data in some embodiments of the present invention.
10 is a view for explaining multidimensional environment time series data among environmental data referred to in some embodiments of the present invention.
11 is a view for explaining a date attribute of environmental data referred to in some embodiments of the present invention.
12 is a diagram for explaining that environmental data is clustered in some embodiments of the present invention.
FIG. 13 is a diagram for explaining representative values representative of a specific environment among environmental data referred to in some embodiments of the present invention. FIG.
FIG. 14 is a view for explaining a storage form of a result of clustering environmental time series data among environmental data in some embodiments of the present invention. FIG.
15 and 16 are views for explaining a classification model referred to in some embodiments of the present invention.
17 is a diagram for explaining that, in some embodiments of the present invention, a regression model for predicting measured time series data of a prediction period is designated for each measurement time series data cluster.
18 is a configuration diagram of a time series data predicting apparatus according to an embodiment of the present invention.

Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS The advantages and features of the present invention and the manner of achieving them will become apparent with reference to the embodiments described in detail below with reference to the accompanying drawings. The present invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art. Is provided to fully convey the scope of the invention to those skilled in the art, and the invention is only defined by the scope of the claims. Like reference numerals refer to like elements throughout the specification.

Unless defined otherwise, all terms (including technical and scientific terms) used herein may be used in a sense commonly understood by one of ordinary skill in the art to which this invention belongs. Also, commonly used predefined terms are not ideally or excessively interpreted unless explicitly defined otherwise. The terminology used herein is for the purpose of illustrating embodiments and is not intended to be limiting of the present invention. In the present specification, the singular form includes plural forms unless otherwise specified in the specification.

For convenience of understanding, before describing the embodiments of the present invention in full, the meanings of the terms used in this specification will be described.

Measured time series data: Indicates time series data of measured values measured by sensors and the like. The measured time series data may be separated by a predetermined period (for example, 24 hours). The sensor may be, for example, a temperature sensor, a brightness sensor, a power consumption sensor connected to a building management system, a temperature sensor, a pressure sensor or the like provided in a production facility, a temperature sensor provided in the computing device, A usage sensor, a storage I / O load sensor, a network usage sensor, and the like. It goes without saying that the sensor capable of generating measurement time series data may include measurement devices other than those illustrated above.

Environmental data: It is data on various environments that may affect the measurement time series data. The environmental data may be classified into i) environmental time series data, ii) environmental representative values, and iii) environmental attribute values. For example, the temperature time series data for 24 hours, the humidity time series data for 24 hours, and the like correspond to the environmental time series data, and the average temperature and the average humidity for each date correspond to the environmental representative value. The holiday / weekdays whether or not the environment property value corresponds to the environmental property value.

Training period: In order to predict time series data, it is necessary to collect data for a certain period of time and learn through techniques such as machine learning. The training period refers to a certain period in the past that is the learning target. The expiration time of the training period may be current. That is, the current data can be collected and collected at the same time as the data is collected. The time series data of the measurement time series data and the environmental data during the training period can be clustered through learning.

Estimation period: Using the learning result during the training period, the measurement time series data of a specific period can be predicted. In the present specification, a prediction target period of measurement time series data is referred to as a prediction period. The prediction period may be a specific period in the future, or may be a specific period in the past for diagnosis of the past period.

Time series data prediction system

Hereinafter, a configuration and operation of a time series data prediction system according to an embodiment of the present invention will be described with reference to FIG. The time series data prediction system according to the present embodiment may include a measurement apparatus 10 and a measurement prediction apparatus 20. [

The measurement apparatus 10 is a device for generating measurement time series data. The measurement apparatus 10 can transmit the generated measurement time series data to the measurement value prediction apparatus 20 and the terminal apparatus 40 via the network. As already mentioned, the measuring device 10 may be, for example, a temperature sensor, a brightness sensor, a power usage sensor connected to a building management system, a temperature, a pressure sensor or the like provided in a production facility, A CPU usage sensor, a memory usage sensor, a storage I / O load sensor, a network usage sensor, and the like.

The environmental data management device 30 generates or collects environmental data that may affect the measurement time series data and provides the environment data to the measurement value prediction device 20. [

The measurement predictor 20 learns the measurement time series data and the environment data during a training period and predicts the measurement time series data during a prediction period using the learning results.

The data learning related operation of the measurement value prediction apparatus 20 will be described below.

As a result of learning about the data during the training period, the measurement time series data of the predetermined period unit is clustered into a plurality of clusters, and representative time series data of each measurement time series data can be determined.

As a result of learning about data during the training period, environmental time series data of a predetermined period unit is clustered into a plurality of clusters, and representative time series data of each environmental time series data can be determined. The measured time series data and the environmental time series data are preferably clustered in the same manner.

Also, as a result of learning about the data during the training period, an optimal classification model for receiving the environment data and outputting clusters of the measurement time series may be generated. Wherein the optimal classification model comprises: i) selecting at least some of the collected plurality of environmental data as factors; and ii) optimally classifying clusters of the measured time series data in a space or plane comprising axes indicating the factors Generating a classification model; iii) determining a performance indicator value of the generated classification model; iv) selecting at least a portion of the plurality of environmental data as a parameter; generating the classification model; And selecting an optimal classification model among the generated classification models based on the performance index value by repeating the selection of the factor while changing the selection of the factor.

Also, as a result of learning about the data during the training period, a regression model for predicting the measurement data from the environmental data may be constructed for each measurement time series cluster. The regression model may be any of a variety of regression models such as, for example, Multivariate Adaptive Regression Splines (MARS) or polynomial regression. Regarding the regression model or regression analysis, since various data such as articles are disclosed, a detailed description of the regression model will be omitted. For example, you can visit the website at https://en.wikipedia.org/wiki/Regression_analysis.

Hereinafter, the measurement time series data prediction related operation of the measurement value prediction apparatus 20 will be described.

The measurement predictor 20 inputs the predicted environmental data predicted value to the optimum classification model to predict a measured time series data cluster in the predicted period. The environmental data predicted value in the prediction period may be weather forecast information such as average temperature, average humidity, average wind speed, and the like. The measurement value prediction apparatus 20 can receive the environmental data prediction value of the prediction period from the environment data management device 30. [

The measurement predictor 20 can transmit information such as representative time series data for the predicted measurement time series data cluster to the terminal device 40 via the network.

If the environmental data time series is included in the factor of the optimal classification model, the measurement prediction apparatus 20 determines that the environmental data time series prediction value (for example, daytime temperature time series prediction value) of the prediction period is obtained as the learning result during the training period Determine which environmental data time series clusters belong to. At this time, the cluster can be quickly determined by comparing the representative time series data of each environmental data time series cluster with the environmental data time series prediction value. The measurement predictor 20 inputs an identifier (for example, an index value) of the determined environmental data time series clusters to the optimum classification model to predict a measurement time series data cluster of the prediction period.

The measurement predictor 20 predicts the time series data of the predicted time series data by using a regression model for the cluster of the predicted measured time series data. The regression model receives time series data for a first environment (e.g., temperature) as a factor, and outputs measured time series data in that case. The regression model may include at least one of a time series data cluster identifier for a second environment (e.g., humidity), a representative value for a third environment (e.g., solar radiation) and environmental attributes (e.g., weekday / One can receive additional input.

The measurement predictor 20 can transmit predicted measurement time series data (e.g., tomorrow's 24 hour energy consumption time series data prediction value) to the terminal device 40 via the network.

1, the measurement data predicting apparatus 20 and the environmental data management apparatus 30 are physically separated from each other. However, in some embodiments, May be configured as one module.

How to predict time series data

Hereinafter, a time series data predicting method according to an embodiment of the present invention will be described with reference to FIGS. 2 to 17. FIG. The method of predicting the time series data according to the present embodiment can be executed by the computing device, for example, by the measurement prediction device 20 described with reference to FIG. The time series data prediction method according to the present embodiment includes an operation of learning data of a training period and an operation of predicting measured time series data of a prediction period using the result of the learning. The operation of learning the training period data will be described with reference to FIGS. 2 to 3, and then the operation of predicting the measured time series data of the prediction period will be described with reference to FIG.

Referring to FIG. 2, measurement time series data of a training period and a plurality of environment data are received (S100, S102). The plurality of environmental data may include time series data or representative values indicating a first environment (e.g., temperature) and time series data or representative values indicating a second environment (e.g., humidity), environmental attributes / Whether it is weekday). Among the received measurement time series data and environment data, environmental time series data are clustered among similar things in the training process (S104, S106). Hereinafter, the clustering process (S104, S106) will be described in detail.

The received measurement time series data is processed in units of the predefined period. For example, if the period is 24 hours, the measured time series data may be separated by 0 hour. The period may be set to a different value according to each measurement time series data. For example, time series data of energy consumption in a building can be separated by 24 hours, and data on the elevator operating distance within a building can be separated by a week.

The measured time series data of each cycle is classified into one of a plurality of clusters through clustering. FIG. 5 is an overlay of energy usage time series data separated by 24 hours. The time series data as shown in Fig. 5 may be clustered by well-known clustering logic such as k-means logic. The k-mean logic is an algorithm for grouping the given data into k clusters, and operates in a way that minimizes the variance of the distance difference with each cluster. The k-means logic is a type of self-learning, and it plays a role of labeling input data that is not labeled. This algorithm has similar structure to clustering using EM algorithm. Since the k-average logic shows excellent performance in clustering with time series data, this embodiment has the effect of improving clustering quality by performing clustering using k-average logic.

On the other hand, according to another embodiment, various clustering logic may be utilized, as well as k-means logic. For information on clustering logic, see the web document 'https://en.wikipedia.org/wiki/Cluster_analysis'.

In one embodiment, after clustering, representative time series data of time series data belonging to each cluster can be selected using time series averaging logic. For example, various well-known time series averaging logic such as DTW Barycenter Averaging (DBA) can be utilized. For DBA logic, see 'F. Petitjean, A. Ketterlin, P. Gancarski, A global averaging method for dynamic time warping, with applications to clustering '. In FIG. 6, the measured time series data of FIG. 5 is clustered with a total of five clusters, and representative time series data of each cluster is extracted.

DBA logic effectively extracts representative time series data in clusters clustered by k-means logic. In this embodiment, the combination of clustering using k-means logic and representative time series data extraction using clustered DBA logic provides the effect of optimal clustering and cluster representative time series data extraction.

FIG. 7 shows a form in which measured time series data of a 24-hour period is stored for each date. As shown in FIG. 7, the measurement time series data for each cycle can be stored together with the cluster index serving as an identifier of the cluster. In addition, representative time series data of each cluster may be stored as a result of clustering. On the other hand, the collected measurement time series data may be multidimensional time series data composed of n (n > = 2) different measurement time series data as shown in Fig.

In performing clustering on time-series data, it is a problem how many clusters are to be clustered. If the number of clusters is too small, low homogeneity of the time series data belonging to each cluster is problematic. If the number of clusters is too large, clustering efficiency becomes poor. Therefore, it is important to determine the proper number of clusters to improve the quality of clustering. In one embodiment of the present invention, the number of clusters is finally determined on the basis of the sum of similarity values between representative time series data for each cluster and measurement time series data of each cycle belonging to each cluster. The similarity sum value can be calculated using difference value calculation logic between various time series data such as DTW distance.

In the case shown in FIG. 9, as the number of clusters is increased from 1 to 5, the DTW distance sum value between the representative time series data of each cluster and the measurement time series data of each cycle belonging to each cluster sharply decreases, If the number of clusters is 5 or more, the decrease in the DTW distance sum becomes small. That is, in the case shown in FIG. 9, even if the number of clusters is increased to 5 or more, the quality of the clustering is not affected. Therefore, in one embodiment, as the number of clusters increases from 1 to k, as the number of clusters increases, the decrease of the sum of DTW distances increases beyond a reference value, and as the number of clusters increases beyond k, If it is less than this criterion, the number of clusters can be finally determined as k.

On the other hand, when the measured time series data is multidimensional time series data composed of two or more individual measured value time series, a Multi-Dimensional Dynamic Time Warping (MD-DTW) between the representative time series for each cluster and the measured time series data of each cycle belonging to each cluster, The number of clusters can be finally determined based on the sum of similarities (e. G., DTW distance) according to the logic. The time series data prediction method according to the present invention provides scalability for multidimensional time series data because clustering and representative time series data of each cluster can be generated in the same way as one dimensional time series data even if the time series data is multidimensional data. That is, in this embodiment, the multidimensional time series data can also be used as a factor for predicting the cluster of the time series data of the measurement time series of the prediction period.

As already mentioned, the time series data in the environment data is also clustered in the same manner as the clustering method of the measurement time series data, and the representative time series data of each cluster is extracted. FIG. 12 shows the results of clustering the temperature time series data of summer and winter and extracting representative time series data of each cluster.

The reason for clustering time series data among environmental data is that the possibility of completely the same data is low due to the nature of time series data. Therefore, the environmental data is clustered so that the time series data of the environment data can be included as a factor for predicting the cluster of the measurement time series data. An identifier (e.g., an index) of each cluster may be used as a factor of an optimal classification model for predicting clusters of measured time series data. Details of the optimum classification model will be described in detail later with reference to Figs. 3, 15 and 16.

10 shows multidimensional environment time series data. For example, in the case of n-dimensional environment time series data including n different environment time series data, clustering as one n-dimensional environment time series data is more preferable than clustering by separating into n one- Lt; RTI ID = 0.0 > environment. ≪ / RTI > Therefore, the multidimensional environment time series data also needs to be used as a factor of the optimal classification model. Clustering and representative time series data can also be extracted from multidimensional environment time series data using the same method as clustering and representative time series data extraction method described above for multidimensional measurement time series data.

As already mentioned, the environmental data collected and learned in some embodiments of the present invention also includes data that is not time series data. For example, data indicating an attribute value of the environment (e.g., whether it is Saturday / weekday / holiday by date in FIG. 11) or data indicating representative values of each environment (for example, temperature / / Average pressure value) can also be included in the environmental data. According to an exemplary embodiment, environment data other than time series data are clustered by a well-known clustering method, and representative values of each cluster can also be extracted.

14 shows a form in which environmental time series data of a 24-hour period is stored for each cycle. As shown in FIG. 14, the environment time series data for each cycle can be stored together with the cluster index serving as an identifier of the cluster. In addition, representative time series data of each cluster may be stored as a result of clustering.

Returning back to Fig. 2, the operation after clustering will be described. When clustering is completed, an optimal model for obtaining clusters of measured time series data is generated (S108). The model indicates an optimal classification model that best classifies clusters of the measurement time series data on a plane or a space formed by at least a part of the plurality of received environmental data as respective axes.

For example, when the first axis is the temperature time series data cluster and the second axis is the humidity time series data cluster, the measurement time series data during the training period is displayed on the plane constituted by the first axis and the second axis , One reference line that best classifies the clusters of the measured time series data on the plane may be displayed. At this time, by using the reference line, clusters of measured time series data can be obtained by inputting temperature time series data clusters and humidity time series data clusters in the prediction period. Therefore, the optimal model for obtaining the clusters of the measured time series data is an optimal classification for best classifying clusters of the measured time series data on the plane or space constituted by at least a part of the received plurality of environment data as the respective axes It is a model.

Referring to FIG. 3, the operation (S108) of generating the optimal classification model will be described in more detail.

First, environment data to be used as a factor among a plurality of environmental data is selected. For example, if the collected environmental data are of three kinds (A, B, C), there are seven kinds of choices (A, B, C, AB, AC, BC, ABC). It is assumed that the measurement time series data does not depend on only one environmental data. If you choose to use two sets of environmental data as parameters, you can construct a plane consisting of two factors and display the time series data of each period over the training period on this plane.

FIG. 15 is a diagram showing a case in which two environment time series data are selected, and on a plane constituted by a first axis indicating a cluster index of the first environment time series data and a second axis indicating a cluster index of the second environment time series data, Is the index number of the cluster. When the data of the training period is processed as shown in Table 1 below, a cluster of the measured time series data can be displayed as shown in FIG.

Cycle First environment time series data
Cluster index Second environment time series data
Cluster index Measured time series data
Cluster index One One One One 2 2 One One 3 One 2 One 4 2 2 One 5 3 One One 6 4 One 2 7 3 2 2 8 One 3 2 9 2 3 2 10 3 3 2 11 4 2 2 12 5 One 2 13 6 One 2 14 5 2 2 15 4 3 2 16 5 3 2 17 6 3 2 18 6 2 2

On the plane shown in FIG. 15, the problem of optimally classifying clusters of measured time series data can be solved by using various classification logic such as SVM (Support Vector Machine) logic and decision tree logic. That is, the embodiment of the present invention uses various classification logic introduced through a web document 'https://en.wikipedia.org/wiki/Statistical_classification', for example, in a plane or a space , It is possible to expand the model to optimally classify the clusters of the measured time series data of each period. However, for convenience of understanding, an embodiment utilizing SVM logic will be described below.

FIG. 16 shows a case in which one environmental time series data (temperature time series data) and one environmental representative value (average humidity) data are respectively selected as factors. As described above, since the environmental time series data can not be displayed on the axis as it is, the first axis indicates the cluster index of the environmental time series data. When the SVM logic is executed, a hyperplane 63 is obtained that can distinguish two heterogeneous data on the plane (measurement time series data of the first cluster and measurement time series data of the second cluster) on the plane. The maximum margin at this time is the distance between two vectors 61 and 62 parallel to the hyperplane as they pass data closest to the hyperplane 63.

16, the hyperplane 63 shown in Fig. 16 is an optimal classification model. However, considering another factor selection, a classification model may be generated that can better classify the measurement time series data of the first cluster and the measurement time series data of the second cluster. In one embodiment, the performance indicator of the classification model is a maximum margin value according to a hyperplane generated according to the SVM logic. The larger the maximum margin, the better the performance indicator.

Therefore, in order to obtain an optimal classification model that best classifies clusters of the measured time series data, a case where the maximum margin value is the largest is searched while various collected environmental data are combined in various ways.

3, the environment data to be used as a parameter in the plurality of environmental data is selected (S180), and a space (when three or more parameters are selected) constituted by axes indicating the selected parameter or a plane (S182), and determines the performance index value of the classification model (when the SVM logic is used, the maximum margin value) (S184). (S186), the selection of the factor is changed (S188), and a classification model is generated using the selected factor (S182) until the performance index value of the generated classification model (S184) is repeated.

When selecting an argument, all cases of selecting at least a part of a plurality of environment data are possible, a range of selectable number of factors can be specified, or the type of selectable data can be limited to a specific type (for example, Environment time series data and environment representative values).

After all the factor selection cases are examined, the performance index values of the classification models generated in the respective factor selection cases are compared, and the classification model having the highest performance index value is selected as the optimal classification model (S189).

Next, as a part of the training work, a regression model is constructed in which environment data of a cycle belonging to the measurement time series data is input for each measurement time series cluster and the measurement time series data is output. That is, the step of constructing the regression model may be performed by using only the data of the period clustered in the first measured value time series data cluster without using the data of the period clustered with the second measured value time series data cluster, And constructing a corresponding regression model. For example, in the case shown in Table 1, when constructing the regression model corresponding to the measurement time series data cluster No. 1, only the environmental data of the cycles 1 to 5 are used.

The construction of the regression model can be performed by applying various logic presented through a web document 'https://en.wikipedia.org/wiki/Regression_analysis' or the like. For example, it may be one of various regression models such as Multivariate Adaptive Regression Splines (MARS) or polynomial regression.

The regression model has first environment time series data of the environment data as a first independent variable. In order to output the measured time series data, at least one time series data which varies with the passage of time must be inputted.

Wherein the regression model includes a cluster identifier of a second environment time series data different from the first environment time series data, a representative value (e.g., an average temperature) representative of a specific environment of each cycle of the environment data, And at least one of data (e.g., whether it is weekday / holiday) as an additional independent variable.

Hereinafter, an operation of predicting measured time series data of a predicted period will be described with reference to FIG.

The environmental data of the forecast period is received (S200). The received environmental data may be a predicted value. The environmental data may be, for example, weather forecast information. The weather forecast information may include, for example, an average temperature of the prediction period, an average humidity, temperature time series prediction data according to time, and the like. It is preferable that the environment data include all data included as a factor of the optimal classification model for time series data to be predicted.

If environmental time series data is included as a factor of the optimal classification model, it is determined in step S202 whether the time series data predicted by the environmental time series data is closest to the clusters of the environmental time series data.

The representative time series data of each cluster is extracted at the time of clustering the environmental time series data (S106). The environmental time series data of the prediction period is not compared with all the data belonging to each cluster but only with the representative time series data of each cluster when determining the cluster corresponding to the environmental time series data of the prediction period (S202). That is, a cluster of the environmental time series data to which the environmental time series data of the prediction period belongs is selected based on the similarity degree according to the difference value calculation logic between the environmental time series data of the prediction period and the representative time series for each cluster of the environmental time series data do.

The degree of similarity may be, for example, various logic for computing a difference value between time series data such as DTW (Dynamic Time Warping) difference arithmetic logic between environmental time series data of the prediction period and representative time series data of each cluster of environment time series data . ≪ / RTI >

For example, if the number of clusters is 10, only 10 DTW values must be compared in the determination of the cluster (S202). Therefore, in this embodiment, the cluster corresponding to the environment time series data of the prediction period can be determined quickly It has an effect.

By inputting the environmental data of the prediction period into the factor of the optimal classification model, a cluster of the measured time series data of the prediction period is predicted (S204). As mentioned above, when the environment time series data is included in the factor of the optimal classification model, the cluster identifier (e.g., cluster index) of the environmental time series data is input instead of the environmental time series data itself.

When the environmental data of the predicted period is input to the regression model corresponding to the cluster of the predicted measured value time series data, the measured time series data of the predicted period can be obtained (S206). As shown in Fig. 17, according to the present embodiment, when the measurement time series data cluster is different, the applied regression model also changes. For example, if the energy usage is forecasted measurement time series data and the energy usage data cluster in the forecast period is predicted as # 1, then the regression model may be model 1 of the MARS (Multivariate Adaptive Regression Splines) model format . If the energy usage data cluster of the forecast period is predicted as # 2, then the regression model will be different for the second model.

On the other hand, if the predicted measurement time series data is different, a regression model of another model type may be applied. For example, FIG. 17 shows that polynomial regression is used for water usage time series data.

The methods according to the embodiments of the present invention described above with reference to Figs. 1 to 17 can be performed by the execution of a computer program embodied in computer readable code. The computer program may be transmitted from a first computing device to a second computing device via a network, such as the Internet, and installed in the second computing device, thereby enabling it to be used in the second computing device. The first computing device and the second computing device all include a server device, a physical server belonging to a server pool for cloud services, and a fixed computing device such as a desktop PC.

The computer program may be stored in a recording medium such as a DVD-ROM, a flash memory device, or the like.

Time series data prediction device

Hereinafter, the configuration and operation of the time-series data predicting apparatus according to another embodiment of the present invention will be described with reference to FIG.

18, the time series data predicting apparatus 20 according to the present embodiment includes a processor 200, a memory 206, a network interface 204, a storage 208, and a system bus 202 . The processor 200, the network interface 204, the storage 208 and the memory 206 transmit and receive data via the system bus 202. The memory 206 loads the computer program for analyzing the measured time series data during the training period and for predicting the measured time series data of the predicted period. The processor 200 executes the computer program loaded in the memory.

The network interface 204 receives measurement time series data and environment data of a training period through a plurality of sensors and a network connected to the environment data management apparatus and receives environmental data of a prediction period, And transmits the prediction result of the measured time series data of the information or the prediction period to the terminal device via the network interface 204. [

The storage 208 stores measurement time series data received via the network interface 204, the environment data and the measurement time series clustering result data 280 inquired by the computer program, environment time series clustering result data 282, And stores the cluster-by-cluster regression model 284.

The measured time series clustering result data 280 includes the result of clustering the measured time series data during the training period and the representative time series data of each cluster.

The environment time series clustering result data 282 includes a result of clustering environment time series data during a training period and representative time series data of each cluster.

The measurement time series data cluster-by-cluster regression model 284 includes configuration information of a cluster-by-cluster regression model of each measurement time series data. The configuration information of the regression model may include regression model type information and a factor list.

The storage 208 may further store information on the optimal classification model for each measurement time series data.

The computer program includes training logic 260 and prediction logic 262.

The training logic 260 includes an operation of clustering measurement time series data of a predetermined period unit during a training period into a plurality of clusters, an operation of collecting a plurality of environmental data during the training period, An operation of selecting at least a part of a parameter as a factor and an operation of generating a classification model that optimally classifies clusters of the measured time series data in a space or a plane constituted by axes indicating the factor, Determining a performance indicator value, selecting the factor, generating the classification model, and determining the performance indicator value by repeating the selection of the factor while changing the performance indicator value to a reference The optimal classification model among the generated classification models .

The prediction logic 262 uses the optimal classification model to predict a cluster of the measured time series data in the prediction period and a prediction model of the prediction time series data using the predicted period And estimating the time series data of the measured value.

In the present specification, the operation is interpreted and executed by the processor 200 and consists of a series of or more instructions that perform a specific function.

While the present invention has been described in connection with what is presently considered to be practical exemplary embodiments, it is to be understood that the invention is not limited to the disclosed embodiments, but, on the contrary, You will understand. It is therefore to be understood that the above-described embodiments are illustrative in all aspects and not restrictive.

Claims (17)

Clustering measurement time series data of a predetermined period unit during a training period into a plurality of clusters;
Collecting a plurality of environmental data during the training period;
Selecting at least a part of the plurality of environmental data as a factor;
Generating a classification model that optimally classifies clusters of the measured time series data on a space or a plane constituted by axes indicating the factor;
Determining a performance indicator value of the generated classification model;
Selecting an optimal classification model among the generated classification models based on the performance index value by repeating the steps of selecting the classification model, generating the classification model, and determining the performance index value, Selecting step; And
And predicting a cluster of the measured time series data in the prediction period using the optimal classification model.
Time series data prediction method. The method according to claim 1,
Wherein the collecting of the environmental data comprises:
And clustering environment time series data of a predetermined period unit of the collected environment data into a plurality of clusters,
When the environment time series data among the selected factors is included, the axis indicating the environmental time series data is an axis indicating the cluster of the environmental time series data,
Time series data prediction method. 3. The method of claim 2,
Wherein clustering the environmental time series data into a plurality of clusters includes:
And clustering in the same manner as the clustering method performed in the step of clustering the measured value time series data into a plurality of clusters.
Time series data prediction method. 3. The method of claim 2,
Wherein the step of predicting the cluster of the measured time series data of the prediction period using the optimal classification model comprises:
Selecting a cluster of the environmental time series data to which the environmental time series data belongs in the prediction period when the environment time series data is included in the factor of the optimal classification model; And
Applying the cluster of environmental time series data of the selected prediction period to the optimal classification model;
Time series data prediction method. 5. The method of claim 4,
Wherein clustering the environmental time series data into a plurality of clusters includes:
Further comprising the step of deriving cluster-wise representative time series data,
Wherein the step of selecting the clusters of the environmental time series data to which the environmental time series data of the prediction period belongs comprises the steps of:
Selecting a cluster of the environmental time series data to which the environmental time series data of the prediction period belongs based on the similarity degree according to the difference value calculation logic between the environmental time series data of the prediction period and the representative time series for each cluster of the environmental time series data / RTI >
Time series data prediction method. 3. The method of claim 2,
The environmental time series data is m-dimensional time series data composed of m (m > = 2) different environment time series data,
Wherein clustering the environmental time series data into a plurality of clusters includes:
Expressing the environmental time series data on a space formed by m axes indicating time axes and m environment time series, respectively; And
And clustering the expressed environmental time series data.
Time series data prediction method. The method according to claim 1,
Wherein the step of generating the classification model comprises:
Generating the classification model using SVM (Support Vector Machine) logic,
Wherein determining the performance indicator comprises:
And using a maximum margin according to a hyperplane generated according to the SVM logic as a performance index of the classification model.
Time series data prediction method. The method according to claim 1,
Wherein the step of estimating the cluster of the measured time series data in the prediction period comprises:
Receiving a prediction value of environmental data used as a factor of the selected classification model among environmental data of the prediction period; And
And inputting the predicted value of the environment data to the optimum classification model to predict a cluster of the measured time series data.
Time series data prediction method. The method according to claim 1,
The step of clustering the measurement time series data into a plurality of clusters,
Clustering each of the measurement time series data into k clusters by applying clustering logic; And
And calculating representative time series data of each cluster using time series averaging logic.
Time series data prediction method. 10. The method of claim 9,
The step of clustering the measurement time series data into a plurality of clusters,
Clustering the k clusters into the k clusters and calculating a representative time series of each of the clusters;
And finally determining the k value based on the sum of similarities between the representative time series data for each cluster and the measured time series data of each cycle belonging to each cluster,
Time series data prediction method. 10. The method of claim 9,
Wherein the measurement time series data is multidimensional time series data composed of two or more individual measurement time series,
The step of clustering the measurement time series data into a plurality of clusters,
Clustering the k clusters into the k clusters and calculating a representative time series of each of the clusters;
Determining the k value based on the similarity between the representative time series data for each cluster and the measured time series data of each cycle belonging to each cluster,
Time series data prediction method. Predicting a cluster of measured time series data of the predicted time period from environmental data of a predicted time period according to an analysis result of measurement time series data and environmental data during a training period; And
And estimating the measured time series data of the prediction period by using a regression model for a cluster of the selected measurement time series data,
Wherein the regression model uses the first environment time series data of the environment data as a first independent variable and the measurement time series data as a dependent variable,
Wherein the regression model includes a first model and a second model,
The step of estimating the time series data includes:
Using the first model when the cluster of the measured time series data is the first cluster and using the second model different from the first model when the cluster of the measured time series data is the second cluster different from the first cluster / RTI >
Time series data prediction method. 13. The method of claim 12,
Wherein the regression model uses a cluster identifier of second environment time series data different from the first environment time series data as a second independent variable,
Time series data prediction method. 13. The method of claim 12,
Wherein the regression model has a representative value representing a specific environment of each cycle of the environmental data as a second independent variable,
Time series data prediction method. 13. The method of claim 12,
Clustering measurement time series data of a predetermined period unit during a training period into a plurality of clusters;
Collecting a plurality of different kinds of environmental data during the training period; And
Further comprising the step of constructing a regression model corresponding to a cluster of each measurement time series data,
The step of constructing the regression model comprises:
And constructing a regression model corresponding to the first measurement time series data cluster using only the data of the period clustered with the first measurement time series data cluster without using the data of the period clustered with the second measurement value time series data cluster doing,
Time series data prediction method. A memory for loading a computer program for analyzing measured time series data during a training period to predict said measured time series data in a predicted period;
A processor for executing the computer program loaded in the memory;
Network interface; And
A storage for storing measurement time series data received via the network interface, the environment data, and data inquired by the computer program,
The computer program comprising training logic and prediction logic,
The training logic comprises:
Clustering measurement time series data of a predetermined period unit during a training period into a plurality of clusters;
Collecting a plurality of environmental data during the training period;
Selecting at least some of the plurality of environmental data as a factor;
Generating a classification model that optimally classifies clusters of the measured time series data in a space or a plane comprising axes indicating the factors;
An operation for determining a performance indicator value of the generated classification model;
Selecting the parameter as the parameter, generating the classification model, and determining the performance indicator value are repeated while changing the selection of the factor, so that the optimal classification of the generated classification model And an operation for selecting a model,
The prediction logic comprises:
And estimating a cluster of the measured time series data in the prediction period using the optimal classification model.
Time series data prediction device. 17. The method of claim 16,
The prediction logic comprises:
Further comprising an operation of predicting the measured time series data of the prediction period using a regression model for a cluster of the predicted measured time series data,
Time series data prediction device.

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