Patent Publication Number: US-2022214956-A1

Title: Estimation device, estimation method and estimation program

Description:
TECHNICAL FIELD 
     The present invention relates to an estimating device, an estimating method, and an estimating program. 
     BACKGROUND ART 
     According to conventional approaches, algorithms such as machine learning and statistical methods can be applied to time-series data observed at regular intervals in time order, so that knowledge about past events can be acquired and predictions for the future can be made. 
     It is assumed that an algorithm for time-series data allows data to be obtained at equal intervals, and if the data is not spaced at equal intervals, missing values are supplemented with imputation values for correction so that the data is arranged at equal intervals. The interval of time-series data should be estimated properly because it cannot be known where data is missing if the data interval is unknown. 
     For example, Non Patent Literature 1 and Non Patent Literature 2 describe technique about prediction of future data for time-series data which is not spaced at equal intervals. 
     CITATION LIST 
     Non Patent Literature 
     [Non Patent Literature 1] “traces-traces 0.4.2 documentation”, [online], 2016, [retrieved Mar. 29, 2019], retrieved from the Internet: &lt;URL:https://traces.readthedocs.io/en/latest/&gt; 
     [Non Patent Literature 2] “Unevenly Spaced Data”, [online], September 2016, IDEO, [retrieved Mar. 29, 2019], retrieved from the Internet: &lt;URL:https://datascopeanalytics.com/blog/unevenly-spaced-time-series/&gt; 
     SUMMARY OF THE INVENTION 
     Technical Problem 
     However, the conventional approaches may have difficulty in properly estimating the interval of time-series data. For example, if missing values exist in time-series data like missing teeth, multiple intervals can be defined between data pieces. Using a narrow interval among the intervals, future data may be predicted with finer granularity, but the prediction accuracy may be significantly lowered because of an increased number of imputation values in past data. Meanwhile, when a wider interval is selected assuming that redundancy values are present, future data can be predicted with high accuracy since less missing values should be imputed by imputation values, but the prediction granularity may be coarse. 
     With the foregoing in view, it is an object of the present invention to properly estimate the interval of time-series data. 
     Means for Solving the Problem 
     In order to solve the problem and achieve the object, an estimating device according to the present invention includes an estimating unit that estimates the median of the intervals between adjacent data pieces in input time series data as a uniform interval for the time-series data, and an extracting unit that extracts data pieces at the estimated uniform intervals from the input time-series data. 
     Effects of the Invention 
     According to the present invention, the interval of time-series data can be properly estimated. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         FIG. 1  is a schematic diagram of a general configuration of an estimating device according to an embodiment of the present invention. 
         FIG. 2  is a diagram for illustrating processing carried out by an estimating unit. 
         FIG. 3  is a diagram for illustrating processing carried out by an extracting unit. 
         FIG. 4  is a diagram for illustrating processing carried out by the extracting unit. 
         FIG. 5  is a flowchart for illustrating an estimating processing procedure. 
         FIG. 6  is a diagram of an exemplary computer which executes an estimating program. 
     
    
    
     DESCRIPTION OF EMBODIMENTS 
     Hereinafter, one embodiment of the present invention will be described in detail with reference to the drawings. Note that the present invention is not limited by the embodiment. In the drawings, the same portions are designated with the same reference characters. 
     Configuration of Estimating Device 
       FIG. 1  is a schematic diagram of a general configuration of an estimating device according to the embodiment. As illustrated in  FIG. 1 , the estimating device  10  according to the embodiment is implemented by a general-purpose computer such as a personal computer and includes an input unit  11 , an output unit  12 , a communication control unit  13 , a storage unit  14 , and a control unit  15 . 
     The input unit  11  is implemented by an input device such as a keyboard and a mouse device and inputs various kinds of instruction information such as the start of processing to the control unit  15  in response to input operation by an operator. The output unit  12  is implemented for example by a display device such as a liquid crystal display and a printing device such as a printer. For example, the output unit  12  indicates a result of estimating processing which will be described. 
     The communication control unit  13  is implemented for example by an NIC (Network Interface Card) and controls communication between an external device and the control unit  15  over a telecommunication line such as a LAN (Local Area Network) and the Internet. For example, the communication control unit  13  controls communication between a management device which manages time-series data and the control unit  15 . 
     The storage unit  14  is implemented by a semiconductor memory device such as a RAM (Random Access Memory) and a flash memory or a storage device such as a hard disk and an optical disk. The storage unit  14  stores a processing program which causes the estimating device  10  to operate or data to be used during execution of the processing program previously or temporarily each time processing is performed. The storage unit  14  may be configured to communicate with the control unit  15  through the communication control unit  13 . 
     For example, the storage unit  14  may store time-series data to be subjected to estimating processing which will be described. In this case, the time-series data is collected for example from the management device which manages time-series data and stored in the storage unit  14  before estimating processing which will be described. 
     Referring back to  FIG. 1 , the control unit  15  is implemented for example by a CPU (Central Processing Unit) and executes the processing program stored in the memory. In this way, the control unit  15  functions as an estimating unit  15   a  and an extracting unit  15   b  as illustrated in  FIG. 1 . These functional units may be implemented in different hardware. The control unit  15  may also include other functional units. 
     The estimating unit  15   a  estimates the median of intervals between adjacent data pieces in input time-series data as a uniform interval for the time-series data. Specifically, the estimating unit  15   a  first accepts input of time-series data to be processed through the input unit  11  or the communication control unit  13 . When the time-series data is collected in advance and stored in the storage unit  14 , the estimating unit  15   a  obtains the time-series data to be processed by referring to the storage unit  14 . 
     The estimating unit  15   a  estimates the interval of the time-series data to be processed. Specifically, the estimating unit  15   a  extracts time intervals between data pieces which constitute the time-series data, specifies the median of the interval values, and estimates the specified median as a uniform interval for the time-series data. In this way, the estimating unit  15   a  can properly estimate a plausible interval as the interval of the time-series data. 
     For example,  FIG. 2  is a diagram for illustrating the processing carried out by the estimating unit  15   a.  In the example shown in  FIG. 2 , two “1s”, six “2s”, and one “4” are extracted as interval values. In this case, the median is the fifth interval value “2” which is the middle value of the nine interval values. Therefore, in the example shown in  FIG. 2 , the estimating unit  15   a  estimates that the interval of the time-series data is “2”. 
     Referring back to  FIG. 1 , the extracting unit  15   b  extracts data pieces at estimated uniform intervals from input time-series data. Here,  FIGS. 3 and 4  are diagrams for illustrating the processing carried out by the extracting unit  15   b.    
     For example, as shown in  FIG. 3 , the extracting unit  15   b  extracts data pieces at time points reached by going back at the estimated uniform intervals in order from the latest data piece of the time-series data. In the example in  FIG. 3 , the extracting unit  15   b  extracts data pieces existing at time points reached by going back at estimated intervals of “2” from the latest data piece of the time-series data at the rightmost end. Then, when there is no data piece to extract, the extracting unit  15   b  ends the processing. 
     Here, the extracting unit  15   b  extracts data pieces which exist at time points reached by going back at the estimated intervals×(1+ε) where ε is a predetermined tolerance ratio. 
     In the example in  FIG. 3 , the extracting unit  15   b  ends the processing at the time point six intervals behind because no data piece exists. In this manner, in the example shown in  FIG. 3 , the extracting unit  15   b  extracts the time-series data including the six data pieces arranged in a time series at intervals of “2”. Therefore, the extracting unit  15   b  can extract the time-series data without a missing value from the input time-series data. Using the time series extracted in this way, future data can be predicted with high accuracy. 
     Alternatively, as shown in  FIG. 4 , the extracting unit  15   b  may extract data pieces which exist at time points reached by going back or advancing at the estimated uniform intervals in order from the time point of each data piece as a reference point in the time series data when the number of the data pieces becomes maximum. 
     The extracting unit  15   b  first selects a reference point. In the example shown in  FIG. 4 , the extracting unit  15   b  selects, as the reference point, the time point of the latest data piece of the time series data indicated by the star mark. The extracting unit  15   b  counts the number of data pieces existing at the time points reached by going back at the estimated intervals in order from the selected reference point. Again, the extracting unit  15   b  counts the number of data pieces existing at the time points reached by going back at the estimated intervals×(1+ε) where ε is the predetermined tolerance ratio. 
     In the example shown in  FIG. 4 , the extracting unit  15   b  counts the number “9” of the nine data pieces arranged in a time series at intervals of “2” except for the missing value indicated by a broken line square in the figure. 
     The extracting unit  15   b  also counts the number of data pieces existing at the time points reached by going back or advancing at the estimated intervals in order from the reference point in a similar manner while changing the reference point. The extracting unit  15   b  counts the number of data pieces existing at the estimated intervals for all the reference points and selects a reference point which allows the number of data pieces to be maximized. Then, the extracting unit  15   b  extracts data pieces existing at the estimated intervals from the selected reference point. 
     The extracting unit  15   b  may output the estimated interval and the extracted time-series data to the output unit  12  or output the data for example to the management device through the communication control unit  13 . 
     Note that the interval for going back/advancing from the reference point is not limited to the interval estimated by the estimating unit  15   a  and may be arbitrarily set. The extracting unit  15   b  can extract time-series data with less missing values from input time-series data for the interval set here. 
     In this way, the extracting unit  15   b  can extract time-series data with the minimum missing ratio from input time-series data. Using the time-series data extracted in this way, future data can be predicted with high accuracy. 
     When the sum of the time periods of data pieces existing with respect to each reference point becomes maximum, the extracting unit  15   b  may extract the existing data pieces. For example, when there are multiple reference points for which the number of data pieces existing at estimated intervals becomes maximum, the extracting unit  15   b  selects a reference point which allows the sum of the time periods of data pieces to be greater and the data pieces to include the most recent data piece. In this way, the extracting unit  15   b  can extract the latest time-series data. 
     Estimating Processing 
     The estimating processing by the estimating device  10  according to the embodiment will be described with reference to  FIG. 5 .  FIG. 5  is a flowchart for illustrating the estimating processing procedure. The flowchart in  FIG. 5  starts for example at the timing when the user makes operation input to instruct the start. 
     The estimating unit  15   a  first specifies the median of the intervals between adjacent data pieces in input time-series data and estimates the specified median as the interval of the time-series data (step S 1 ). 
     Then, the extracting unit  15   b  extracts data pieces existing at the estimated median intervals from the input time-series data (step S 2 ). 
     For example, the extracting unit  15   b  extracts data pieces at the time points reached by going back at the estimated uniform intervals in order from the latest data of the time-series data. In this case, the extracting unit  15   b  extracts data pieces existing at the time points reached by going back at the estimated intervals×(1+ε) where ε is a predetermined tolerance ratio. 
     Alternatively, when the time point of each data piece in the time-series data is used a reference point, and the number of data pieces existing in order from the reference point to the time point reached by going back or advancing at the estimated uniform intervals becomes maximum, the existing data pieces are extracted. When the sum of the time periods of data pieces existing with respect to the reference point becomes maximum, the extracting unit  15   b  further may be configured to extract the existing data pieces. Again, the extracting unit  15   b  extracts data pieces which exist at time points reached by going back at the estimated intervals×(1+ε) where ε is the predetermined tolerance ratio. This completes the series of estimating processing steps. 
     As described above, in the estimating device  10  according to the embodiment, the estimating unit  15   a  estimates the median of the intervals between adjacent data pieces in the input time-series data as a uniform interval for the time-series data. The extracting unit  15   b  extracts data pieces at the estimated uniform intervals from the input time-series data. 
     This allows the estimating device  10  to estimate appropriate intervals with less missing values from input time-series data and extract time-series data. Using the time-series data extracted in this way, future data can be predicted with high accuracy. In this manner, the estimating device  10  can properly estimate the interval of the time-series data. 
     For example, the extracting unit  15   b  extracts data pieces at time points reached by going back at the estimated uniform intervals in order from the latest data piece of time-series data. This allows the estimating device  10  to extract time-series data without missing values from the input time-series data and predict future data with high accuracy. 
     When the time point of each data piece in time-series data is used as a reference point, and the number of data pieces existing at time points reached by going back or advancing at estimated uniform intervals in order from the reference point becomes maximum, the existing data pieces may be extracted. This allows the estimating device  10  to extract the time series data with the smallest missing ratio from the input time series data and to predict future data with high accuracy. 
     When the sum of the time periods of data pieces existing with respect to the reference point becomes maximum, the extracting unit  15   b  may extract the existing data pieces. This allows the estimating device  10  to extract the latest time series data from the input time-series data and to predict future data with even higher accuracy. 
     Program 
     The processing executed by the estimating device  10  according to the embodiment can also be created into a program which describes the processing in a computer-executable language. According to one embodiment, the estimating device  10  may be implemented by installing, on a desired computer, an estimating program for executing the above-described estimating processing as package software or on-line software. For example, when an information processing apparatus may be caused to execute the estimating program described above, the information processing apparatus can function as the estimating device  10 . The information processing apparatus as used here includes a desktop or notebook personal computer. Alternatively, the information processing apparatus includes a mobile communication terminal such as a smartphone and a PHS (Personal Handyphone System) and a slate terminal such as a PDA (Personal Digital Assistant). The function of the estimating device  10  may also be implemented in a cloud server. 
       FIG. 6  is a diagram of an exemplary computer which executes the estimating program. The computer  1000  for example has a memory  1010 , a CPU  1020 , a hard disk drive interface  1030 , a disk drive interface  1040 , a serial port interface  1050 , a video adapter  1060 , and a network interface  1070 . These components are connected by a bus  1080 . 
     The memory  1010  includes a ROM (Read Only Memory)  1011  and a RAM  1012 . The ROM  1011  stores for example a boot program such as BIOS (Basic Input Output System). The hard disk drive interface  1030  is connected to the hard disk drive  1031 . The disk drive interface  1040  is connected to the disk drive  1041 . For example, a removable storage medium such as a magnetic disk or an optical disk is inserted into the disk drive  1041 . The serial port interface  1050  is connected for example to a mouse device  1051  and a keyboard  1052 . For example, a display  1061  is connected to the video adapter  1060 . 
     Here, the hard disk drive  1031  stores for example an OS  1091 , an application program  1092 , a program module  1093 , and program data  1094 . The various kinds of information in the above description of the embodiment are stored for example in the hard disk drive  1031  or the memory  1010 . 
     The estimating program is also stored in the hard disk drive  1031  for example as the program module  1093  which describes commands to be executed by the computer  1000 . Specifically, the program module  1093  which describes various kinds of processing to be executed by the estimating device  10  in the above description of the embodiment is stored in the hard disk drive  1031 . 
     Data used for information processing by the estimating program is stored for example in the hard disk drive  1031  as the program data  1094 . Then, the CPU  1020  reads out the program module  1093  and the program data  1094  stored in the hard disk drive  1031  into the RAM  1012  as required and executes the above-described procedures. 
     Note that the program module  1093  and the program data  1094  related to the estimating program are not necessarily stored in the hard disk drive  1031  but may be stored in a removable storage medium and read out by the CPU  1020  through the disk drive  1041 . Alternatively, the program module  1093  and the program data  1094  related to the estimating program may be stored in another computer connected over a network such as a LAN or a WAN (Wide Area Network) and read out by the CPU  1020  through the network interface  1070 . 
     Although the embodiments to which the invention made by the present inventors is applied have been described, the invention is not limited by the description and drawings which constitute part of the disclosure of the invention according to the embodiments. In other words, all other embodiments, examples, operation techniques, and other features made for example by the person skilled in the art on the basis of the embodiments fall within the scope of the present invention. 
     REFERENCE SIGNS LIST 
     
         
           10  Estimating device 
           11  Input unit 
           12  Output unit 
           13  Communication control unit 
           14  Storage unit 
           15  Control unit 
           15   a  Estimating unit 
           15   b  Extracting unit