JP5602283B1 - Prediction device, prediction method, and computer program - Google Patents

Abstract

A prediction apparatus, a prediction method, and a computer program for predicting a value related to an event that changes in time series are provided.
Recording means for associating values related to events that change in time series with item values related to a plurality of related events related to the events in time series, logarithmically converting the values related to the events Means for converting the item value into a secondary variable for the logarithmically converted value, a derivation means for deriving the strength of the correlation using the value and the item value related to the event after the conversion, and the strength of the derived correlation And a prediction means for calculating a predicted value related to the event in the prediction target period.
[Selection] Figure 10

Description

  The present invention relates to a prediction apparatus, a prediction method, and a computer program for predicting a value related to an event that changes in time series.

  Economic forecasts such as product demand forecasts and sales forecasts are extremely important in examining the direction and strategy in company management. Then, how to connect the predicted demand to the plans of each department such as sales, inventory, production, distribution, and development is a management issue. Furthermore, not only forecasts for economic events such as demand forecasts and sales forecasts, but forecasting future events using information that has changed over time is an important issue in various fields. .

  Various time-series analysis methods have been proposed as methods for predicting subsequent changes in time-series events such as the number of products demanded or the number of products sold. Examples of such an analysis method include multivariate analysis such as multiple regression analysis and T method (for example, Patent Document 1 and Non-Patent Document 1). Furthermore, various application proposals have been proposed for these analysis methods (Non-Patent Document 2, etc.).

Japanese Patent No. 3141164

Edited by Kazuo Tatebayashi, Shoichi Teshima, Ryoko Hasegawa, “Introductory MT System”, Nikka Giren Shuppansha, December 2008 Yukiya Masuda, “Research of T Method Considering Nonlinear Components”, Proceedings of 17th Quality Engineering Research Conference, p. 422-425, 2009

  The T method is not originally a method for prediction of time series data. There is no particular concept of the time axis, and it is a method for selecting which factor has the most influence on the value of an event related to a large number of factors. When the T method is used as a time series data prediction method, the relationship between the item value of each item and the signal value in a certain period is specified, and the signal value in the next period is predicted. For this reason, there has been a problem that estimation errors in the item values of the respective items overlap to deteriorate the prediction accuracy of the signal values.

  The present invention has been made in view of such circumstances, and an object of the present invention is to provide a prediction device, a prediction method, and a computer program capable of improving prediction accuracy by reflecting a tendency of a signal to change over time. .

The prediction device according to the present application records a value related to an event that changes in time series and an item value of each item that includes each of a plurality of related events related to the event in a time series as a record. A value related to the event in a first period within a period recorded in the recording means, and an item value related to the related event in a second period before the first period. A prediction device that predicts a value related to the event in a prediction target period based on the strength of the correlation, and a unit that logarithmically converts the value related to the event and relates to the item for the logarithmically converted value Factor effect indicating the strength of correlation of each item with respect to a change in the value related to the event, using the means for converting the item value into a secondary variable, the value related to the event after the conversion, and the item value related to the item Means for calculating the value and the factor effect value calculated is large A means for calculating the correlation strength with the prediction target for each of a plurality of items selected in order from the item number, and an item for maximizing the correlation strength with the prediction target based on the calculated correlation strength A means for determining the number, a means for selecting items for the determined number of items in descending order of the factor effect value, and a prediction relating to the event in the prediction target period based on the item values of the selected items And a predicting means for calculating a value.

The prediction method of the present application is a recording in which a value related to an event that changes in time series and an item value of each item that includes each of a plurality of related events related to the event are associated and recorded in time series A value relating to the event in the first period within the period recorded in the recording means by the computer provided with the means, and an item relating to the related event in the second period before the first period. A prediction method for predicting a value related to the event in a prediction target period based on the strength of correlation with the value, wherein the computer logarithmically converts the value related to the event, The item value related to the item is converted into a secondary variable, and the strength of the correlation of each item with respect to the change of the value related to the event is calculated using the value related to the event and the item value related to the item after the conversion. The factor effect value shown is calculated and the calculated factor effect Calculate the strength of the correlation with the prediction target for each item selected in descending order of the value for each number of items, and maximize the strength of the correlation with the prediction target based on the calculated correlation strength The number of items to be determined is determined, the items corresponding to the determined number of items are selected in descending order of the factor effect value , and based on the item value of the selected item, the predicted value related to the event in the prediction target period is determined. It is characterized by calculating.

The computer program of the present application records a time series in which values related to events that change in time series are associated with item values of each item having each of a plurality of related events related to the events as items. And a value related to the event in the first period within the period recorded in the recording means, and an item related to the related event in the second period before the first period. A computer program for predicting a value related to the event in a prediction target period based on the strength of correlation with the value, wherein the computer is configured to logarithmically convert the value related to the event to a logarithmically converted value. On the other hand, using the means for secondary variable conversion of the item value related to the item, the value related to the event after conversion, and the item value related to the item, the correlation strength of each item with respect to the change in the value related to the event The A means for calculating the factor effect value, a means for calculating, for each number of items, the strength of correlation with the prediction target for a plurality of items selected in descending order of the calculated factor effect value, and a strength of the calculated correlation. And a means for determining the number of items that maximizes the strength of correlation with the prediction target, a means for selecting items for the determined number of items in order from the largest factor effect value, and Based on the item value of an item, it is made to function as a prediction means for calculating a predicted value related to the event in the prediction target period.

  In the present application, from the recording means for recording the value related to the event that changes in time series and the item value of each item having each of the related events related to the event as items, in time series, The value related to the event and the item value of each item are read, logarithmic conversion is performed on the value related to the event, and secondary variable conversion is performed on the item value for the logarithmically converted value. Since logarithmic transformation is performed on the signal (value related to the event), and the second-order variable transformation is performed on the item after the logarithmic transformation, the third-order or higher-order accidental error of the item on the signal is removed, and up to the second-order component. By setting it as the main effect, dispersion | distribution is stabilized about both a signal and an item.

  According to the present application, by removing the third or higher order coincidence error of an item with respect to a signal and using a secondary component as a main effect, dispersion can be stabilized for both the signal and the item, and prediction accuracy can be improved. .

It is explanatory drawing which shows the signal used by T method, and the example of the content of each item. It is explanatory drawing which shows an example of the proportionality constant (beta) and SN ratio (eta) (square ratio) of each item used by T method. It is the figure which showed the actual signal value of each member or the actual value of the standardized signal value, and the comprehensive estimated value calculated | required about each member in list format. It is a factor effect figure which shows the S / N ratio of the comprehensive estimated value of each item in T method. It is a graph which shows the influence with respect to the factor effect of each item in T method. It is explanatory drawing which illustrates a time difference model notionally. It is a graph which shows notionally the processing for determining the number of item selection. It is a graph which shows a response | compatibility with the number of item selections, and SN ratio of a comprehensive estimated value. It is a block diagram which shows the structure of the prediction apparatus which concerns on this Embodiment. It is a flowchart which shows an example of the prediction process procedure by a prediction apparatus. It is a flowchart which shows the process sequence of variable conversion. It is a flowchart which shows the calculation procedure of SN ratio of a comprehensive estimated value. It is explanatory drawing which shows the content of the item in a present Example. It is a graph which shows the relationship between the signal value before variable conversion, and an item value. It is a graph which shows the relationship between the signal value after variable conversion, and an item value. It is a graph which shows a response | compatibility with the number of item selections, and SN ratio of a comprehensive estimated value. It is a graph which shows the accuracy evaluation of the prediction result by the prediction method of this application. It is a graph which shows accuracy evaluation of the prediction result by the prediction method of a comparative example. It is a graph which shows accuracy evaluation of the prediction result by the prediction method of a comparative example.

The present invention will be specifically described with reference to the drawings showing the embodiments thereof.
The prediction method of the present application is based on a method using an MT system (for example, T method) that has been used in the past, in order to predict a change over time of a value (signal) related to an event that changes in time series, A time difference model is introduced. In the present application, item values each having a plurality of related events that may cause a signal are recorded in time series in association with the signal, and prediction is made based on the strength of correlation between the signal and the item value. One of the features is to select signals and items to be used.

  In addition, the following prediction methods are implemented by calculation means, such as a computer (prediction apparatus mentioned later) which can read each data from the recording means which has recorded the data used for prediction.

First, an outline of the T method which is the basis of the prediction method of the present application will be described. FIG. 1 is an explanatory diagram showing examples of signals used in the T method and the contents of each item. In FIG. 1, a member is an index indicating data for each unit period, and is indicated as 1, 2,..., N. The value relating to the event is recorded as a signal value M. “Item 1”, “Item 2”,..., “Item k” indicate each of a plurality of related events related to the signal value M. X ₁₁ , X ₁₂ ,..., X _1k indicate item values of each item related to the signal value M ₁ of member 1. For example, if the member has monthly electricity usage, the monthly electricity usage (W) is the signal value M, and the item values are “month”, “temperature”, “wind speed”, “precipitation ( Record the variables that would be related to the ups and downs of electricity usage, such as “monthly average”, “sunshine hours (monthly average)”, “maximum temperature (monthly average)”, “minimum temperature (monthly average)”.

  In addition, although the actual value may be used for the signal value M and each item value X, it is preferable to use them after normalization using a calculation means. In the normalization method, for example, the calculation means subtracts the average value of the item value of each item (for example, the average value of the monthly average temperature of all members to be used) from the item value X of each item (for example, the monthly average temperature). There is a way to keep it. Thereby, when the item value is plotted against the signal value (referred to as a unit space), the prediction formula can be expressed as a straight line passing through the origin.

  In the T method, a specific formula that specifies the relationship between the signal value M of each member and the related multi-variable item value is obtained, the item value of the next unit period to be predicted is predicted, and the predicted item A value is applied to a specific formula to predict. However, the item data is multivariate. For example, there may be 12 members and there are 20 items (k = 20). In such a case, it is difficult to obtain a specific expression for predicting the next signal value. In the T method, for each of a large number of items, a value indicating the strength of the factor effect with respect to signal value fluctuation is evaluated, and a value indicating the factor effect is weighted. Thereby, by selecting effective items, it is possible to perform prediction with sufficiently high accuracy by using the selected items without using data of all items.

  Therefore, in the T method, the calculation means calculates the proportionality constant β and the SN ratio η (square ratio) for each item using the members having the signal value M by applying the following formulas 1 and 2. To do. The S / N ratio is a value indicated by using the reciprocal of dispersion as shown in Equation 2, is the sensitivity of the signal value for each item, and indicates the strength of correlation between each item and the signal value.

  In addition, the above-mentioned Formula 1 and Formula 2 are formulas for obtaining the proportionality constant β and the SN ratio η (square ratio) for Item 1. The calculation means performs the same calculation as for item 1 for item 2 to item k. FIG. 2 is an explanatory diagram showing an example of the proportionality constant β and SN ratio η (square ratio) of each item used in the T method. In FIG. 2, the proportionality constant β and SN ratio η (square ratio) for each item calculated by applying Equation 1 and Equation 2 described above to each item are shown in a table format.

  Next, using the proportionality constant β and the SN ratio η (square ratio) for each item, an estimated value of the output for each item is obtained for each member by the computing means. For the i-th member, the estimated output value of item 1 can be expressed by the following equation 3. Similarly, the estimated value of the output from item 2 to item k is obtained by the calculation means.

  Thereby, the calculating means can derive the comprehensive estimation formula (Formula 4) as a prediction formula indicating the relationship between the item value and the total estimated value of the signal value. However, the comprehensive estimation formula using all items (1 to k) with respect to the signal value to be predicted does not have the highest prediction accuracy. Therefore, in order to increase the contribution to the influence on the prediction target and increase the prediction accuracy, an appropriate combination of items is selected from all items by the calculation means.

Therefore, the calculation means calculates an overall estimated value using the SN ratios η ₁ , η ₂ ,..., Η _k (square ratio), which is the estimation accuracy for the estimated value of each item, as a weighting coefficient. Therefore, the total estimated value of the i-th member can be expressed by the following Equation 4.

  FIG. 3 is a diagram showing, in a list form, the actual signal values of each member or the actual values of the standardized signal values and the total estimated values obtained for each member. Then, using the actual value and the total estimated value of each member obtained as shown in FIG. 3, the computing means calculates the SN ratio η (db) of the total estimated value for each item by the following equation 5. .

  In the T method, a method of obtaining a value called a factor effect value by using the total estimated value obtained for each item as described above, and selecting an item based on the factor effect value is used. FIG. 4 is a factor effect diagram showing the SN ratio of the overall estimated value of each item in the T method, and FIG. 5 is a graph showing the influence of each item on the factor effect in the T method.

In FIG. 4, the horizontal axis indicates items to be selected, and the vertical axis indicates the SN ratio of each item by taking the SN ratio of the comprehensive estimated value. In FIG. 4, for each item, the SN ratio of the total estimated value for each data including the data of the item, that is, the strength of the correlation with the signal value is shown on the left side, and each data excluding the data of the item is shown on the right side. The SN ratio of the comprehensive estimated value with respect to is shown. In the example shown in FIG. 4, there are 36 items, and there are 2 ³⁶ combinations of selections. The computing means derives the SN ratio of the prediction target for one or a plurality of items for each of these combinations. And a calculating means calculates the average value of SN ratio of the combination containing the said item used as object, and the average value of S / N ratio of the combination which does not contain the said item. In FIG. 4, the average value of the SN ratio including the data of the item calculated in this way is shown on the left side, and the average value of the SN ratio not including the item is shown on the right side for each item.

  In FIG. 5, the horizontal axis indicates items to be selected, and the vertical axis indicates the factor effect value, and the degree of the factor effect value for each item is indicated. The factor effect value on the vertical axis in FIG. 5 is the left side of the SN ratio on the right side (excluding items) of the SN ratio of the overall estimated value of each item indicating the strength of the correlation of the prediction target in FIG. Of the SN ratio (including items), that is, a value obtained by subtracting the right SN ratio from the left SN ratio. That is, for each item, the degree of effect on the S / N ratio when the item is included is not shown. Therefore, an item for which the calculated factor effect value is positive indicates that the SN ratio of the comprehensive estimated value is increased by using the item. In a method called a two-sided T method, only items having such a positive factor effect value are selected, and analysis by the T method as described above is performed.

  When prediction is performed using the T method (two-sided T method) as described above, the actual prediction accuracy still does not increase. Therefore, the inventor first applied the concept of a time difference model to the above-described T method, and performed conversion processing taking into account the nonlinearity between the signal value and the item value. Instead of the method of selection, a method of maximizing the SN ratio of the comprehensive estimated value is used.

Next, the time difference model introduced by the prediction method of the present application will be described.
The T method is not originally a method for prediction of time series data. There is no particular concept of the time axis, and it is a method for selecting which factor has the most influence on the value of an event related to a large number of factors. When the T method is used as it is in the prediction method, as described above, the relationship between the item value and the signal value for each item of each member is specified, and the signal value of the next member is predicted. Therefore, in order to predict the signal value of the next member, the item value for each item of the next member must be predicted. It is easily assumed that the estimation error of the item value of each item at this time overlaps and the prediction accuracy of the signal value is deteriorated. In addition, when predicting the next signal value based on time-series data, especially in fields such as economic forecasting or sales forecasting, there should be signs of future events. is there. Accordingly, the inventor decided to associate the association between the signal value and the item value for each item as described above not by the members of the same period but by the ones shifted by a predetermined time. Specifically, the computing means that implements the prediction method of the present embodiment associates the signal value after a predetermined period with the item value for each item in a certain period, and calculates Equations 1 to 5 between these values. And the signal value after a predetermined period is estimated (predicted) based on the item value for each item in the present (most recent) period using the comprehensive estimation formula obtained as a result.

FIG. 6 is an explanatory diagram conceptually illustrating the time difference model. FIG. 6 shows the passage of time from the left to the right in the figure. Each rectangle in the lower part of FIG. 6 indicates an item value of each item for each year, and each rectangle in the upper part indicates a signal for each year at the same time as each item. The time difference model, rather than associating the item value of the signal and each item in the same period, with respect to the signal M _i, item values of the items of predetermined period before _{X it 1, X it 2,} ..., X it k (In the example of FIG. 6, t = 1 year) is associated.
In the example of the time difference model shown in FIG. 6, the signal of one year is configured to correspond to the item value of the previous year. However, these periods are not limited to the periods shown in FIG. It is possible.

In the prediction method of the present embodiment, the calculation means uses the signal to which the time difference model is applied and the item value of each item as the signal value M ₁ and the item values X ₁₁ , X ₁₂ ,. The T method is applied by applying the relationship of X _1k . The signal value of the prediction target period is obtained by inputting the item value of each item before the predetermined period (the item value from January to December 2009 in the example of FIG. 6) into the comprehensive estimation formula (Formula 4). Ask. That is, in the time difference model in the prediction method of the present embodiment, the computing means specifies the relationship between the item values of a plurality of items and the signal values after a predetermined period, thereby determining the future signal from the past or current item value. It is possible to predict the value.

  In the present embodiment, in order to predict a 2010 signal (unknown signal) that is a prediction target period, signals of each year (known signal) from 2005 to 2009, and 2004 to 2008 are used. It was set as the structure which introduces the time difference model which matches the item (known item) of each year until. Each item in 2009 (that is, the item corresponding to the predetermined period before the prediction target period) is already acquired data, but is not used in the time difference model. This is called “unknown item”.

  In the prediction method of the present embodiment, the best prediction scheme is adopted in addition to the time difference model described above. In the best prediction scheme, items are subjected to second-order variable transformation for logarithmically transformed signals, dispersion is stabilized for both signals and items, and predicted values are selected by item selection that maximizes the total estimated S / N ratio in the T method calculation. Is the way to get.

  In the following, variable conversion performed in the best prediction scheme will be described. In the conventional T method, for each item, a straight line that passes through the zero point is set for the relationship between the item data and the signal value, and weighting based on the deviation from this straight line (numericalization as an SN ratio) is performed. In such a linear transformation, items that have less variation with respect to a straight line are items that contribute to changes in the signal value, and items that have more variation, that is, items that have no correlation between the change in the item value and the change in the signal value, This item does not contribute to the change in value. However, the item values of all items do not necessarily have a straight line, that is, a linear relationship in relation to the signal value. Therefore, in the best prediction scheme, logarithmic transformation is performed on the signal, and the item is subjected to secondary variable transformation on the logarithmically transformed signal.

  In the above-described time difference model, the unknown signal corresponding to the unknown item is unknown, so variable conversion of the unknown signal cannot be performed. For this reason, in the present embodiment, the conversion coefficient (that is, the coefficient of the secondary term, the coefficient of the primary term, and the constant) when the variable conversion is performed on the known item is used to convert the variable of the item value in the unknown item. To implement.

  Next, item selection will be described. FIG. 7 is a graph conceptually showing a process for determining the number of item selections. First, the time difference model is applied, and the signal-to-variable conversion signal value and the item value for each item are applied to calculate the SN ratio of the overall estimated value of each item by applying Equations 1 to 5. Then, as described with reference to FIGS. 4 and 5, the calculation means calculates a factor effect value for each item from the SN ratio of the comprehensive estimated value. As shown in FIG. 7, the computing means first sets the minimum value of the factor effect value for each item as an initial threshold value. The computing means selects an item whose factor effect value is equal to or greater than a threshold value, and calculates the SN ratio of the comprehensive estimated value with respect to the signal value (for example, all 1 to n). If the threshold is the initial value, all items are selected. Next, the calculation means sets a value obtained by adding a predetermined value to the threshold value as the next threshold value, similarly selects an item whose factor effect value is equal to or greater than the threshold value, and calculates the SN ratio of the comprehensive estimated value with respect to the signal value. . The calculation means repeats such processing until the threshold reaches the maximum value of the factor effect value, that is, reaches the line shown as MAX in FIG. The ratio can be calculated for each number of items. The calculation means sets the initial value of the threshold value to the maximum value MAX or more, decreases the threshold value by a predetermined value, selects an item that is a factor effect value above each threshold value, and calculates the SN ratio of the comprehensive estimated value. You may make it go.

  FIG. 8 is a graph showing the correspondence between the number of item selections and the SN ratio of the overall estimated value. The graph shown in FIG. 8 shows the transition of the SN ratio of the total estimated value with respect to the number of item selections, with the SN ratio (db) of the total estimated value on the vertical axis and the number of item selections on the horizontal axis. . Among the S / N ratio values, the open square is the S / N ratio of the overall estimated value when an item is selected using the two-sided T method of selecting an item having a positive factorial effect value. Open diamonds indicate the SN ratio of the overall estimated value when all items are selected. White circles indicate the number of items with the maximum total SN ratio. As shown in FIG. 8, in the method of selecting an item having a positive factor effect value, the SN ratio of the comprehensive estimated value does not become maximum. In the present embodiment, by implementing the method shown in FIG. 7, the number of items for which the SN ratio of the overall estimated value is the highest is determined, and the items for the determined number of items in descending order of the factor effect value. The method of selecting is adopted.

  As described above, the present inventor applies a time difference model to the T method, calculates a signal-to-noise ratio of the overall estimated value after performing a conversion process considering the nonlinearity between the signal value and the item value of each item, The knowledge that prediction accuracy can be improved by applying the selection method of the item which maximizes the S / N ratio was obtained. Hereinafter, application examples of the present invention will be specifically disclosed.

  FIG. 9 is a block diagram showing a configuration of the prediction apparatus 1 according to the present embodiment. The prediction device 1 is a computer such as a personal computer or a server computer, and includes a control unit 10, a recording unit 11, a temporary storage unit 12, an input unit 13, and an output unit 14.

  The control unit 10 includes, for example, a CPU (Central Processing Unit). The control part 10 controls the operation | movement of each part of the hardware mentioned above based on the prediction program 2 demonstrated below, and exhibits the function as the prediction apparatus 1 which concerns on this Embodiment. In addition, the control part 10 has a function as a calculating means which implements the prediction method mentioned above.

  The recording unit 11 uses a nonvolatile memory such as a ROM (Read Only Memory) or a hard disk drive. The recording unit 11 may be an external hard disk drive, an optical disk drive, or another recording device connected via a communication network. That is, the recording unit 11 is a general term for one or a plurality of information recording media accessible from the control unit 10.

  The recording unit 11 records a prediction program 2 including various procedures for realizing the prediction method of the present embodiment. A part of the recording area of the recording unit 11 is used as a database (DB) 110 that records signal values and data of a plurality of items corresponding to the values (item values of each item). The control unit 10 can read and write signal values and item values of each item with respect to the database 110. The database 110 records the signal value of each member and the item value of each item in time series, for example, in the format shown in FIG.

  The temporary storage unit 12 is, for example, a nonvolatile memory such as a DRAM (Dynamic Random Access Memory) or an SRAM (Static Random Access Memory). The temporary storage unit 12 temporarily stores information generated by the processing of the control unit 10.

  The input unit 13 receives a user operation input using a keyboard, a mouse, or the like.

  The output unit 14 uses a display unit such as a liquid crystal monitor or a printing unit such as a printer, and outputs information processing results by the control unit 10.

  In the prediction apparatus 1 configured as described above, the control unit 10 executes a process based on the prediction program 2 to predict a signal value related to a future event. FIG. 10 is a flowchart illustrating an example of a prediction processing procedure performed by the prediction device 1.

  The control unit 10 receives input of the signal value and the item value of each item related thereto from the input unit 13, and records the received signal value and the item value for each item in the database 110 of the recording unit 11 (step S11). ). The signal values and item values for each item to be recorded in the database 110 may be input from other devices via the communication network as well as those input from the input unit 13 or other information recording media. May be input.

  The control unit 10 generates a time difference model based on the signal value recorded in the database 110 of the recording unit 11 and the item value for each item (step S12). The time difference model generated in step S12 is a model in which the item value for each item is associated with the signal value after a predetermined period of the item value of the item, as described with reference to FIG. That is, in step S12, the control unit 10 associates the signal value stored in time series with the item value for each item while shifting by a predetermined period.

  The control unit 10 performs variable conversion of the signal value and the item value for each item based on the relationship between the corresponding signal value and the item value for each item in the generated time difference model (step S13). The variable conversion processing procedure will be described in detail with reference to the flowchart of FIG.

  Based on the signal value after variable conversion and the item value of each item associated with the time difference model, the control unit 10 uses the above-described Equations 1 and 2 to calculate the proportionality constant β and the SN ratio η (2) for each item. (Multiplier ratio) is calculated (step S14).

  The control unit 10 calculates the estimated value of the output by Expression 3 for each member in the selected signal period using the proportional constant β and the SN ratio η (square ratio) for each item using Expression 3. Step S15).

  The control unit 10 calculates an overall estimated value using the SN ratio (square ratio), which is the estimation accuracy of the estimated value, as a weighting coefficient by using Equation 4 (step S16).

  Next, based on Equation 5, the control unit 10 calculates the SN ratio (db) of the overall estimated value of each item based on the signal value and the overall estimated value (step S17).

  The control unit 10 derives a factor effect value for each item (step S18). As described above, the factor effect value is the SN ratio of the overall estimated value with respect to the item value of each item including the item with respect to the SN ratio of the overall estimated value with respect to the item value of each item excluding the item. Calculated by obtaining the difference. The S / N ratio of the comprehensive estimated value is the strength of correlation of the item value of the item with respect to the signal value, and is a value expressed as a logarithm of a value proportional to the reciprocal of the variance.

  Next, the control unit 10 calculates, for each item number, the SN ratio of the comprehensive estimated value with respect to the item values of the plurality of items selected in descending order of the factor effect value (step S19). Details of the processing in step S19 have been described above with reference to FIG.

  The control unit 10 determines the number of items that maximizes the SN ratio based on the SN ratio of the comprehensive estimated value for each number of items (step S20).

  The controller 10 selects as many items as the number of items determined in step S20 (step S21). Then, the control unit 10 selects the item value among the item values for each item before the predetermined period associated with the prediction target period in the time difference model, that is, the item value for each item in the corresponding period closest to the prediction target period. Is applied to Equation 4 to calculate a predicted value (step S22). Β and η in Equation 4 are those calculated in step S14 (corresponding to the time difference model and calculated by the variable-converted signal value and the item value of each item). In step S <b> 22, the predicted value is output from the output unit 14 or recorded in the recording unit 11. If the signal value is normalized, it can be obtained by performing inverse transformation.

FIG. 11 is a flowchart showing a variable conversion processing procedure. The control unit 10 performs logarithmic conversion on each signal value M _i (i = 1, 2,..., N) (step S131). That is, the control unit 10 calculates m _i = log M _i when the signal value after logarithmic conversion is m _i (i = 1, 2,..., N).

Next, the control unit 10 performs secondary variable conversion of the item values X _{it j} (j = 1, 2,..., K) of each item associated with the time difference model with respect to the signal value m _i after logarithmic conversion ( Step S132). Specifically, the control unit 10, 2 ascribes next signal value m _i after logarithmic transformation for an item value X _{it j} of each item, only the transform coefficients (the second-order terms the number of items coefficients, first-order term of Coefficient, constant). Then, the control unit 10, on a regression curve obtained for each item, obtains a signal value corresponding to the item value X _{it j} of each item, a temporary storage unit that signal value as the item value x _{it j} after conversion 12 The process to memorize to etc. is performed.

Next, the control unit 10 performs variable conversion on the item value of the unknown item (step S133). In step S131, the converted signal value m _i can be calculated for the known signal, and in step S132, the converted item value x _{it j} for each item can be calculated. However, although the above-described unknown item exists as an item value, there is no corresponding unknown signal (predicted signal), and thus the item value after variable conversion cannot be obtained. For this reason, in this embodiment, the conversion coefficient obtained for the known item is diverted, and the variable conversion is performed for the item value of the unknown item.
The unknown item after the variable conversion is required in the step of calculating the predicted value in the above-described step S22. Therefore, the unknown item may be converted into a variable after the item is selected in step S21.

  FIG. 12 is a flowchart showing a procedure for calculating the SN ratio of the comprehensive estimated value. The control unit 10 sets a value equal to or smaller than the minimum value of the factor effect value derived in step S16 as an initial value of the threshold value (step S171). Step S171 is a state in which an initial value is set in FIG. As shown in FIG. 7, the processing amount can be reduced by setting the minimum factor effect value as a threshold value.

  Next, the control unit 10 selects an item for which a factor effect value equal to or greater than the set threshold is calculated (step S172). When the minimum value of the factor effect value is set as the initial value of the threshold value, all items are selected in the first step S172.

  Next, the control unit 10 calculates the strength of the correlation of the prediction target with respect to the data of the item selected in step S172, that is, the SN ratio of the total estimated value (step S173), and calculates the SN ratio of the calculated total estimated value. Then, it is recorded in the temporary storage unit 12 in association with the number of items (step S174).

  Next, the control unit 10 determines whether or not the calculation of the SN ratio of the comprehensive estimated value for the selected item has been completed (step S175). Step S175 is a process for determining whether to end the repetitive process, and is a process for determining whether or not the calculation process of the SN ratio of the total estimated value for each item number has been completed for each item number. For example, if the set threshold value is greater than or equal to the maximum factor effect value, and if a factor effect value that exceeds the threshold value does not exist due to resetting of the threshold value described later, a comprehensive estimate for each calculated number of items Completion conditions such as when the number of values matches the number of items to be selected can be set as appropriate.

  If it is determined in step 175 that calculation of the SN ratio of the comprehensive estimated value for the selected item is not completed and calculation of the SN ratio of the comprehensive estimated value for a smaller number of items is required (S175: NO), the control unit 10 resets the set threshold value to a value larger by a predetermined value (step S176), proceeds to step S172, and repeats the subsequent processing.

  If it is determined in step S175 that the calculation of the SN ratio of the comprehensive estimated value for the selected item has been completed (S175: YES), the control unit 10 ends the process according to this flowchart.

  Here, the mode in which the initial value of the threshold value is set to be equal to or less than the minimum value of the factor effect value and the threshold value is reset so as to increase by a predetermined value is shown, but the reverse processing may be performed. . In other words, the initial value of the threshold is set to be equal to or greater than the maximum value of the factor effect value, the item for which the factor effect value equal to or greater than the set threshold is calculated is selected, and the threshold value is reset so as to decrease by a predetermined value. May be.

Hereinafter, specific examples to which the prediction method by the prediction device 1 is applied will be described.
FIG. 13 is an explanatory diagram showing the contents of items in this embodiment. The items shown in FIG. 13 are items representing the number of shipments of various agricultural machines, and are recorded as monthly data. For example, the item “tractor” represents the total number of shipments divided into classes of less than 20 ps, 20 to 30 ps, 30 to 50 ps, and 50 ps or more. In the present embodiment, “combine” (that is, the number of shipments including both the self-removal type and the normal type) is selected as the prediction target signal, and the prediction target signal is predicted based on the other 26 series items. It was.

  FIG. 14 is a graph showing the relationship between signal values and item values before variable conversion. The horizontal axis of FIG. 14 shows the number of standardized combine machines (logarithmic value), and the vertical axis shows the standardized number of combine harvesters (logarithmic value). That is, the relationship between the logarithmically converted signal value and the item value (raw value) of a specific item before variable conversion is shown. In the example shown in FIG. 14, one unit space is set. Specifically, the average value of all the signal values is calculated, the signal sample closest to the average value is set as the unit space signal value, and the item value corresponding to the signal sample is set as the unit space item value. Yes.

FIG. 15 is a graph showing the relationship between signal values and item values after variable conversion. The horizontal axis of FIG. 15 indicates the number of standardized combine harvesters (logarithmic value), and the vertical axis indicates the standardized number of combine harvesters (logarithmic value). The graph shown in FIG. 15 is obtained by subjecting item values to secondary variable conversion with respect to the signal values shown in FIG. Since the item value is subjected to secondary variable conversion for the logarithmically converted signal value, the item value is also a logarithmic value.
One feature of the present application is to stabilize the dispersion of both the signal and the item by removing the third or higher order coincidence error of the item with respect to the signal and using the second order component as the main effect.

  FIG. 16 is a graph showing the correspondence between the number of item selections and the SN ratio of the overall estimated value. In the graph shown in FIG. 16, the SN ratio (db) of the total estimated value is taken on the vertical axis, and the number of item selections is taken on the horizontal axis, and the transition of the SN ratio of the total estimated value with respect to the number of item selections is shown. . For the signal, the number of shipments of “combine” is selected from the items shown in FIG. 13, the remaining 26 series are selected as items, and the SN ratio (db) of the total estimated value is calculated according to the flowchart shown in FIG. Show.

In the graph of FIG. 16, white diamonds indicate the SN ratio of the overall estimated value when all items are selected, and in this example, it was 17.2 db.
A white square indicates the SN ratio of the comprehensive estimated value when an item having a positive factor effect value is selected. In this example, the factor effect values were positive: “combine (self-removal type)”, “control machine”, “dryer”, “cultivator”, “power duster (control machine)”, There were nine items of “normal type (combine)”, “cutter”, “rice mill”, and “rice selector”, and the S / N ratio was 18.9 db.

  White circles indicate values at which the S / N ratio (total S / N ratio) of the total estimated value is maximized. In this example, the S / N ratio of the overall estimated value was maximized when three items of “combine (self-removal)”, “control machine”, and “dryer” were selected. The ratio was 19.3 db.

  According to the statistical data of the Japan Agricultural Machinery Industry Association, the combine is the sum of “self-destructing” and “ordinary”, and is the harvesting machine for rice and wheat. The control machine is used for weeding and pest control in summer, and the dryer is used for drying, which is a post-harvest process. Therefore, it is reasonable that the above-described three types of agricultural machines are selected by the item selection method that maximizes the SN ratio of the overall estimated value. However, in the conventional method of selecting items for which the factor effect is positive, the SN ratio of the overall estimated value decreases, and there are six types other than “combine (self-removal type)”, “control machine”, and “dryer”. Agricultural machinery is selected. In particular, it is unreasonable to select a “cultivator” for field cropping and a “cutter” for feed cutting.

  Hereinafter, the effects of the variable conversion process, the item selection method, and the single space setting according to the present application will be described. FIG. 17 is a graph showing the accuracy evaluation of the prediction result by the prediction method of the present application. In the graph shown in FIG. 17, the horizontal axis represents the item selection method, the left vertical axis represents the predicted SN ratio (db), and the right vertical axis represents the average absolute percentage error (MAPE). The transition of the SN ratio (db) and MAPE of the prediction according to is shown. Note that FIG. 17 shows the accuracy of the prediction result derived by setting one unit space.

A black circle shown in FIG. 17 indicates the SN ratio of the prediction derived using the logarithmically converted signal value and the item value not subjected to variable conversion, and the white circle indicates the MAPE. In FIG. 17, the case where the prediction is performed without performing the variable conversion is shown as the result of the zeroth-order variable conversion. Similarly, a black triangle indicates a signal value obtained by logarithm conversion and an S / N ratio of a prediction derived using an item value obtained by performing primary variable transformation on the signal value obtained by logarithmic transformation, and a white triangle indicates the MAPE. Yes.
The black squares are predictions derived using the signal values and item values obtained by the variable transformation of the present application (that is, the logarithmically transformed signal values and the logarithmically transformed signal values are the secondary variable transformed item values). The S / N ratio and the open square indicate the MAPE.

  According to FIG. 17, it can be seen that the SN ratio and MAPE of the prediction are greatly improved as the order of the variable conversion of the item is increased. In addition, when all items are adopted, and when items selected with the number of items that maximize the total SN ratio are adopted compared to the case where items with positive factor effect values are adopted, the predicted SN ratio It can be seen that MAPE is improved.

  As described above, the prediction accuracy is greatly influenced by the order of item variable conversion and the item selection method. Therefore, it is necessary to examine a prediction scheme for the influence of conditions such as signal logarithmic conversion and unit space setting method.

  FIG. 18 is a graph showing the accuracy evaluation of the prediction result by the prediction method of the comparative example. FIG. 18 shows the accuracy of the prediction result when the signal is adopted as a raw value and prediction is performed by applying the two-sided T method. Taking the item selection method on the horizontal axis, the SN ratio (db) for prediction on the left vertical axis, and the average absolute percentage error (MAPE) on the vertical axis on the right, the SN ratio (db for prediction depending on the item selection method) ) And MAPE. FIG. 18 shows the accuracy of the prediction result derived by setting one unit space.

  The black circles shown in FIG. 18 indicate the SN ratio of the prediction derived using the raw signal values and item values, and the white circles indicate the MAPE. Similarly, a black triangle indicates a signal value that is a raw value and an S / N ratio of a prediction derived by using an item value obtained by converting the signal value to a primary variable, and a white triangle indicates the MAPE. . A black square indicates a signal value that is a raw value and a predicted SN ratio derived by using an item value obtained by performing secondary variable conversion on the signal value, and a white square indicates the MAPE.

According to FIG. 18, it is understood that when the raw value signal is adopted and the two-sided T method is applied, the prediction accuracy is not improved even if the primary or secondary variable conversion is performed on the item. Note that, in the T method (0th order) without variable conversion, the prediction S / N ratio and MAPE are most improved when an item with positive factor effect is selected.
However, it is understood that when the raw value signal is adopted and the two-sided T method is applied, the prediction accuracy is deteriorated as compared with the prediction scheme to which the logarithmic transformation of the signal shown in FIG. 17 is applied.

  FIG. 19 is a graph showing the accuracy evaluation of the prediction result by the prediction method of the comparative example. FIG. 19 shows the accuracy of the prediction result when the logarithmic conversion is performed on the signal value and the variable conversion of the item value is performed on the converted signal value. FIG. 19 is different from FIGS. 17 and 18 in that the average value of the signal and each item is used as the unit space value.

  The horizontal axis of FIG. 19 shows the item selection method, the vertical axis on the left side shows the predicted SN ratio (db), and the vertical axis on the right side shows the average absolute percentage error (MAPE). In FIG. 19, black circles, black triangles, and black squares use item values that are not subjected to variable conversion, item values that are subjected to primary variable conversion, and item values that are subjected to secondary variable conversion with respect to logarithmically converted signal values. The SN ratio of the derived prediction is shown. White circles, white triangles, and white squares indicate the respective MAPEs.

  According to FIG. 19, although the effect on the prediction accuracy due to the increase in the order of the variable conversion of the item is small, the SN ratio and the MAPE of the prediction are slightly improved when the order of the variable conversion of the item is increased. I understand. However, when the unit space is set to an average value, it can be seen that the prediction accuracy is worse than when one unit space shown in FIG. 17 is set.

As described above, in the present embodiment, (1) logarithmic conversion is performed on a signal, and second-order variable conversion is performed on the logarithmically converted signal, thereby stabilizing dispersion for both the signal and the item. (2) A prediction scheme characterized in that one unit space is set, and (3) the number of items that maximizes the total estimated SN ratio is determined by T-method calculation, and item selection is performed according to the determined number of items. I decided to adopt it.
By adopting such a prediction scheme, it is possible to significantly improve the prediction accuracy as compared with the conventional T method.

  The embodiment disclosed this time is to be considered as illustrative in all points and not restrictive. The scope of the present invention is defined by the terms of the claims, rather than the meanings described above, and is intended to include any modifications within the scope and meaning equivalent to the terms of the claims. In addition, the technical features described in each embodiment can be combined with each other.

DESCRIPTION OF SYMBOLS 1 Prediction apparatus 2 Prediction program 10 Control part 11 Recording part 12 Temporary storage part 13 Input part 14 Output part 110 Database

Claims (3)

A recording unit that records a value related to an event that changes in time series and an item value of each item that includes each of a plurality of related events related to the event in a time-series manner; Based on the strength of correlation between the value related to the event in the first period within the period recorded in the means and the item value related to the related event in the second period before the first period. A prediction device for predicting a value related to the event in the prediction target period,
Means for logarithmically converting the value related to the event;
Means for secondary variable conversion of the item value related to the item with respect to the logarithmically converted value;
Means for calculating a factor effect value indicating the strength of correlation of each item with respect to a change in the value related to the event, using the value related to the event after conversion and the item value related to the item ;
Means for calculating, for each number of items, the strength of correlation with the prediction target for a plurality of items selected in descending order of the calculated factor effect value;
Means for determining the number of items that maximizes the strength of the correlation with the prediction target based on the calculated strength of the correlation;
Means for selecting items for the determined number of items in descending order of the factor effect value;
A prediction device comprising: a prediction unit that calculates a predicted value related to the event in the prediction target period based on an item value of a selected item . By a computer provided with a recording means for recording a value related to an event that changes in time series and an item value of each item that includes each of a plurality of related events related to the event in time series And a strong correlation between the value related to the event in the first period within the period recorded in the recording means and the item value related to the related event in the second period before the first period. And a prediction method for predicting a value related to the event in the prediction target period,
The computer
Logarithmically transform the value related to the event,
For the logarithmically converted value, convert the item value related to the item to a secondary variable,
Using the value related to the event after conversion and the item value related to the item , a factor effect value indicating the strength of correlation of each item with respect to a change in the value related to the event is calculated,
Calculate the strength of the correlation with the prediction target for each of the items selected in descending order of the calculated factor effect value for each number of items,
Based on the calculated correlation strength, determine the number of items that maximizes the correlation strength with the prediction target,
In order from the largest factor effect value, select items for the determined number of items,
A prediction method, comprising: calculating a prediction value related to the event in the prediction target period based on an item value of a selected item . In a computer provided with a recording unit that records a value related to an event that changes in time series and an item value of each item that includes each of a plurality of related events related to the event in time series And a strong correlation between the value related to the event in the first period within the period recorded in the recording means and the item value related to the related event in the second period before the first period. And a computer program for predicting a value related to the event in the prediction target period,
The computer,
Means for logarithmically converting the value relating to the event,
Means for secondary variable conversion of the item value related to the item with respect to the logarithmically converted value;
Means for calculating a factor effect value indicating the strength of correlation of each item with respect to a change in the value related to the event, using the value related to the event after conversion and the item value related to the item ;
Means for calculating, for each number of items, the strength of correlation with the prediction target for a plurality of items selected in descending order of the calculated factor effect value;
Means for determining the number of items that maximizes the strength of the correlation with the prediction target based on the calculated strength of the correlation;
Means for selecting items for the determined number of items in descending order of the factor effect value;
A computer program for causing a computer to function as a prediction unit that calculates a predicted value related to the event in the prediction target period based on an item value of a selected item .

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