JP7065685B2 - Data shortage presentation system and data shortage presentation method - Google Patents

Description

The present invention relates to a technique for determining whether or not there is a lack of data for constructing a prediction model and presenting a determination result.

There is a technique to predict future events based on the tendency of past data. For example, a prediction model is used to predict an event. The prediction model represents the relationship between the explanatory variable corresponding to the cause and the objective variable corresponding to the prediction result, and makes it possible to infer the objective variable from the explanatory variable. For example, in the case of a multiple regression equation, the explanatory variable and the objective variable refer to variables that are related to each other and can represent the objective variable by weighting a plurality of explanatory variables.

Past data (hereinafter, also simply referred to as “data”) is a combination of the value of the explanatory variable and the value of the objective variable observed in the past. If a prediction model is constructed using the necessary data, the prediction model will have high prediction accuracy. However, if the data is insufficient, the prediction accuracy of the prediction model will decrease.

Therefore, in order to make highly accurate predictions, it is necessary to check whether the data necessary for constructing the prediction model is prepared, and to add data if there is a lack of data.

Patent Document 1 discloses a method of presenting the reliability of an estimation model by evaluating the variance of the estimation error with respect to the estimation model, thereby enabling a reconstruction judgment of the estimation model.

Patent Document 2 discloses a method of determining the quantity and quality of missing data in a prediction model and displaying advice to a user to make up for the missing data.

Japanese Unexamined Patent Publication No. 2011-44592 Japanese Patent Application Laid-Open No. 2015-219651

The technique disclosed in Patent Document 1 presents information for determining whether or not the estimation model should be reconstructed by evaluating the variance of the estimation error with respect to the estimation model and presenting the reliability of the estimation model. Is. However, the technique disclosed in Patent Document 1 determines the range of explanatory variables for which data is insufficient, and does not present information based on the determination result. Therefore, it is not possible to know whether or not the data necessary for constructing the estimation model is prepared.

Further, the technique disclosed in Patent Document 2 only displays advice to the user to make up for the lack of data in the prediction model, and quantitatively ranges the range of the explanatory variables in which the data is lacking. The method for determining is not disclosed in Patent Document 2.

An object of the present invention is to provide a technique for quantitatively determining a range of values of explanatory variables for which data is insufficient and presenting a determination result.

The data shortage presentation system according to one aspect of the present invention is a data shortage presentation system that presents whether or not there is a lack of data necessary for estimating the objective variable from the explanatory variables, and is a data shortage presentation system, and the value of the explanatory variable and the value of the objective variable. The data area of the data given as a combination with is divided into a plurality of areas determined by the value of the explanatory variable and the value of the objective variable, and the value of each divided area or the explanatory variable is within a predetermined range. A data shortage range calculation unit that calculates at least one index value related to data shortage for each region group and determines whether or not there is a data shortage for each range of the explanatory variables based on the index value, and the data shortage. It has an output unit that outputs the determination result of the range calculation unit.

According to the present invention, it is possible to quantitatively determine the range of values of the explanatory variables for which data is insufficient, and present the determination result.

It is a figure which shows the data lack presentation system by this embodiment. It is a flowchart for demonstrating the process in the data deficiency presentation system shown in FIG. It is a figure which shows an example of the division of the data area in the data shortage presentation system shown in FIG. It is a figure which shows the structure of the data shortage area determination process shown in FIG. It is a flowchart for demonstrating the detail of the data amount shortage area determination process shown in FIG. It is a figure which shows the specific example of the data amount shortage area determination process shown in FIG. It is a flowchart for demonstrating the detail of the data area low accuracy determination process shown in FIG. It is a figure which shows the specific example of the data area low accuracy determination process shown in FIG. It is a flowchart for demonstrating the detail of the input / output high-sensitivity area determination process shown in FIG. It is a figure which shows the specific example of the input / output high-sensitivity area determination process shown in FIG. 6 is a diagram showing a region determined to have no data shortage in the examples shown in FIGS. 6, 8 and 10. It is a flowchart for demonstrating the process in the output part shown in FIG. It is a figure which shows the specific example of the determination result in the output part in the case which considered the output request range given from the output range request part in the data shortage presentation system shown in FIG. It is a figure which shows an example of the screen which is displayed and output by the output part shown in FIG. It is a figure which shows an example of the screen which is displayed and output by the output part shown in FIG. It is a figure which shows an example of the screen which is displayed and output by the output part shown in FIG. It is a figure which shows an example of the screen which is displayed and output by the output part shown in FIG.

Hereinafter, embodiments of the present invention will be described with reference to the drawings.

FIG. 1 is a diagram showing a data shortage presentation system according to the present embodiment.

As shown in FIG. 1, this embodiment has a data shortage range calculation unit 2, an output range request unit 4, and an output unit 3, and a region where data is insufficient when creating an estimation model or a prediction model is provided. The purpose is to make a judgment and present how to judge the lack of data and improve the accuracy. The lack of data refers to data in which the objective variable cannot be determined with a certain accuracy with respect to the explanatory variable.

The data shortage range calculation unit 2 determines whether or not there is a shortage of data necessary for estimating the objective variable from the explanatory variables, and the data 1 accumulated in the past is given. Data 1 is a data set of two or more variables having a combination of the value of the explanatory variable and the value of the objective variable. The data shortage range calculation unit 2 divides the data area of the data 1 given as a combination of the value of the explanatory variable and the value of the objective variable into a plurality of areas determined by the value of the explanatory variable and the value of the objective variable. At least one index value related to data shortage is calculated for each divided area or for each area group in which the value of the explanatory variable is within a predetermined range, and the presence or absence of data shortage is determined for each range of the explanatory variable based on the index value. to decide. As a result, the data shortage range calculation unit 2 calculates in which area the data necessary for estimating the objective variable from the explanatory variables is lacking, and what the reason for the shortage is based on.

The output range request unit 4 accepts the range of the objective variable that the user wants to acquire as a prediction target as the output request range and notifies the output unit 3.

The output unit 3 presents the determination result of the data shortage range calculation unit 2 by display output. At that time, the output unit 3 presents the area and the reason for determining the data shortage in the output request range notified from the output range request unit 4 with respect to the judgment result in the data shortage range calculation unit 2. .. Further, when there are a plurality of explanatory variables, the output unit 3 presents the number of regions determined to have insufficient data for each range of the objective variables by display output for each of the plurality of explanatory variables.

The processing in the data shortage presentation system configured as described above will be described below.

FIG. 2 is a flowchart for explaining the processing in the data shortage presentation system shown in FIG.

In the data shortage presentation system shown in FIG. 1, when presenting the presence or absence of data shortage necessary for estimating the objective variable from the explanatory variables, first, when the data 1 is given, the data shortage range calculation unit 2 performs the data shortage range calculation unit 2. One explanatory variable of data 1 is selected (step 101), and the data area for the selected explanatory variable is divided into a plurality of areas determined by the value of the explanatory variable and the value of the objective variable (step 102). At this time, a method of division is determined so that the given data 1 is dispersed, and the area of the data 1 is divided into a plurality of areas based on the method.

FIG. 3 is a diagram showing an example of division of a data area in the data shortage presentation system shown in FIG.

For example, as shown in FIG. 3, in the data shortage range calculation unit 2, the data area of one selected explanatory variable x1 and the objective variable y is divided into a plurality of areas by a grid at equal intervals as shown by a broken line. Is possible. In the figure, x marks indicate data points. In addition, the division of the data area is not limited to the grid at equal intervals, but if it is a method that can observe the variation of data points, such as changing the width of the grid according to the data or specifying the range with a circular shape. Not limited.

Next, in the data shortage range calculation unit 2, the presence or absence of data shortage is determined by determining the data shortage area for all the explanatory variables of the given data 1 as the data shortage area determination process. A determination is made (steps 103 and 104), and all determination results and determination areas are output to the output unit 3 (step 105).

FIG. 4 is a diagram showing the configuration of the data shortage area determination process 103 shown in FIG.

As shown in FIG. 4, the data shortage area determination process 103 shown in FIG. 3 is composed of a data amount shortage area determination process 201, a data area low accuracy determination process 202, and an input / output high sensitivity area determination process 203. ing. The data shortage area determination process 103 determines whether or not there is a data shortage by executing the data amount shortage area determination process 201, the data area low accuracy determination process 202, and the input / output high sensitivity area determination process 203. .. At that time, in the data amount shortage area determination process 201, the data density is calculated as an index value, in the data area low accuracy determination process 202, the data deviation is calculated as an index value, and in the input / output high sensitivity area determination process 203. Calculates the ratio of the change amount of the objective variable to the change amount of the explanatory variable as an index value. The order of the data amount insufficient area determination process 201, the data area low accuracy determination process 202, and the input / output high sensitivity area determination process 203 can be arbitrarily set.

FIG. 5 is a flowchart for explaining the details of the data amount shortage area determination process 201 shown in FIG.

When executing the data amount shortage area determination process 201 shown in FIG. 4 in the data shortage area determination process 103 shown in FIG. 3, first, an area group consisting of the data areas divided in step 102 shown in FIG. 2 is selected. It is selected (step 301), and the data density of the selected region group is calculated as an index value (step 302).

Then, it is determined whether or not the calculated data density is below a certain value, which is a predetermined threshold value (step 303), and if it is below a certain value, it is determined that there is a data shortage due to a lack of data amount (step 304). ), If it is not below a certain level, it is determined that the amount of data is not insufficient and there is no data shortage (step 305). The user can arbitrarily set a certain value for determining whether or not the amount of data is insufficient, or a value based on statistical processing can be used. The data density refers to the number of data for the selected data area.

This determination is performed in all data areas (step 306), and the data shortage range calculation unit 2 outputs the determination result and the determination area for each area group to the output unit 3 (step 307).

FIG. 6 is a diagram showing a specific example of the data amount shortage area determination process 201 shown in FIG.

For example, as shown in FIG. 9, a fixed value for determining whether or not the amount of data is insufficient in each of a region group consisting of a plurality of regions divided by a broken line at regular intervals with respect to the explanatory variable. When is set to 2 points or less, it is determined that the amount of data is insufficient in the area group surrounded by the solid line.

In this way, by determining whether or not the amount of data is insufficient based on the density of the data of the plurality of regions, it is quantitatively determined that the amount of data is insufficient in a specific range of the explanatory variables. Can be presented.

FIG. 7 is a flowchart for explaining the details of the data area low accuracy determination process 202 shown in FIG.

When the data area low accuracy determination process 202 shown in FIG. 4 is executed in the data shortage area determination process 103 shown in FIG. 3, first, an area group consisting of the data areas divided in step 102 shown in FIG. 2 is selected. It is selected (step 401), and the data deviation in the selected region group is calculated as an index value (step 402).

Then, it is determined whether or not the calculated data deviation is above a certain level, which is a predetermined threshold value (step 403), and if it is above a certain level, it is determined that there is a lack of data due to the low accuracy of the data (step). 404) If it is not above a certain level, it is determined that there is no data shortage because the data is not low accuracy (step 405). It should be noted that a certain value for determining whether or not there is a lack of data can be arbitrarily set by the user, or a value based on statistical processing can be used. Further, the data deviation means the variation of the data within the selected region group, and the low accuracy means that the variation is large.

This determination is performed in all data areas (step 406), and the data shortage range calculation unit 2 outputs the determination result and the determination area for each area group to the output unit 3 (step 407).

FIG. 8 is a diagram showing a specific example of the data area low accuracy determination process 202 shown in FIG.

For example, as shown in FIG. 8, the data distribution outputs a certain value for determining whether or not there is a data shortage in a plurality of areas divided by a broken line at regular intervals with respect to the explanatory variables. When it is decided to straddle the five areas in, the area surrounded by the solid line is determined to have low data accuracy.

In this way, by determining whether the accuracy of the data is low based on the deviation of the data between the regions of the plurality of regions, it is quantitatively determined that the accuracy of the data is low in a specific range of the explanatory variables. And can be presented.

FIG. 9 is a flowchart for explaining the details of the input / output high-sensitivity area determination process 203 shown in FIG.

When the input / output high-sensitivity area determination process 203 shown in FIG. 4 is executed in the data shortage area determination process 103 shown in FIG. 3, first, a straight line is drawn between the data to obtain the inclination between the data. (Step 501), and the ratio of the change amount of the objective variable to the change amount of the explanatory variable of the data in the region group is calculated as an index value (step 502).

Then, a region group consisting of the data regions divided in step 102 shown in FIG. 2 is selected (step 503), and the ratio of the change amount of the objective variable to the change amount of the explanatory variable of the data in the selected region group is determined. It is determined whether or not it is above a certain level, which is a predetermined threshold (step 504), and if it is above a certain level, there is a lack of data due to the high sensitivity that the objective variable changes rapidly with respect to the explanatory variables of the data. It is determined (step 505), and if it is not above a certain level, it is determined that there is no data shortage because the sensitivity is not high (step 506). It should be noted that a certain value for determining whether or not there is a lack of data can be arbitrarily set by the user, or a value based on statistical processing can be used. Further, as the approximate interpolation between the data, interpolation by any approximation method such as linear approximation or quadratic approximation can be used.

Then, when it is judged that the sensitivity of the data is high in the area group, the recommended area for which data should be acquired is set so that the ratio of the change amount of the objective variable to the change amount of the explanatory variable of the data in the area group is less than a certain value. Calculate (step 507).

This determination and calculation are performed in all data areas (step 508), and the data shortage range calculation unit 2 outputs the determination result and the determination area for each area group to the output unit 3 together with the calculated recommended area (step 508). 509).

FIG. 10 is a diagram showing a specific example of the input / output high-sensitivity region determination process 203 shown in FIG.

In the input / output high-sensitivity area determination process 203, for example, as shown in FIG. 10, the ratio of the change amount of the objective variable to the change amount of the explanatory variable of the data is 4 sections of the objective variable with respect to Δx for 1 section of the explanatory variable. In the case of the determination condition that the change of the minute Δy is large, the region straddling these is determined to have high sensitivity of data.

When it is determined that the sensitivity is high, the recommended area of the data point is set to fill the space between the data so that the ratio of the change amount of the objective variable to the change amount of the explanatory variable of the data in the area group is less than a certain value. It will be calculated.

In this way, by determining whether the data sensitivity is high based on the input / output sensitivity of the objective variable to the explanatory variable in the region group, it is quantitatively determined that the data sensitivity is high in a specific range of the explanatory variable. Can judge and present. Further, in a highly sensitive region where the objective variable changes sensitively with respect to the explanatory variable, the range of the explanatory variable for which data should be acquired is recommended, so that data can be added efficiently.

By the above-mentioned data amount shortage area determination process 201, data area low accuracy determination process 202, and input / output high sensitivity area determination process 203, the data shortage range calculation unit 2 is based on a plurality of index values and has a plurality of types of data for different reasons. It will be judged whether or not there is a shortage of.

FIG. 11 is a diagram showing a region determined to have no data shortage in the examples shown in FIGS. 6, 8 and 10.

By performing the processing in the examples shown in FIGS. 6, 8 and 10, as shown in FIG. 11, the area determined to have no data shortage is determined.

FIG. 12 is a flowchart for explaining the processing in the output unit 3 shown in FIG.

In the output unit 3 shown in FIG. 1, the data amount shortage area determination result 601 which is the judgment result of the data shortage area determination process 103 in the data shortage range calculation unit 22, the data low accuracy area determination result 602, and the input / output height When the sensitivity area determination result 603 is input, the determination result within the output request range 604 given by the output range request unit 4 is output (step 605), and the number of areas determined to have insufficient data is used as an explanatory variable. It is presented for each (step 606) and used as a determination history (step 607).

Further, the output range is selected according to the output request range 604 (step 608), it is determined whether or not there is an explanatory variable determined to have no data shortage for each area (step 609), and if there is no explanatory variable, it is determined. The result is output (step 610), and the same processing is performed for all the explanatory variables within the output request range (step 611). On the other hand, if there is an explanatory variable for which it is determined that there is no data shortage, the same processing is performed for all the explanatory variables within the output request range without outputting the determination result.

After that, if it is determined that there is data shortage in all the areas within the output request range (step 612), the area determined to have data shortage and the reason for the shortage are determined for each explanatory variable. It is presented by displaying and outputting (step 613), and if there is no data determined to be insufficient, the process ends as it is. Further, the area determined to have data shortage, the reason for the shortage, and the number of areas determined to have data shortage, which is the determination history in step 607, are recorded.

FIG. 13 is a diagram showing a specific example of the determination result in the output unit 3 when the output request range given by the output range request unit 4 is taken into consideration in the data shortage presentation system shown in FIG. 1.

When the presence or absence of data shortage is determined for each data area group by the above-mentioned data amount shortage area determination process 201, data area low accuracy determination process 202, and input / output high sensitivity area determination process 203, as shown in FIG. In the output unit 3, the type of data shortage is specified for the output request range for each area group of the data. In this example, the type of specified data shortage is single in each region group, but in reality, there may be multiple types such as data shortage and low accuracy.

FIG. 14 is a diagram showing an example of a screen displayed and output by the output unit 3 shown in FIG.

When the presence or absence of data shortage is determined for each data area group by the above-mentioned data amount shortage area determination process 201, data area low accuracy determination process 202, and input / output high sensitivity area determination process 203, as shown in FIG. In the output unit 3, the range determined to have data shortage and the reason for the lack of data in the range are associated and displayed for each area group of the explanatory variables. If the reason for the lack of data is due to high sensitivity, the recommended area of data calculated in order to eliminate the high sensitivity determination in the input / output high sensitivity area determination process 203 is displayed. For example, in the example shown in FIG. 10, since it is determined that there is a lack of data due to high sensitivity in the range of X ₁₂ to X ₁₄ , X _12'to X included in the range of X ₁₂ to X ₁₄ The narrow range of _13'will be displayed as the recommended area of data.

In this way, by displaying the range of the explanatory variables and the reason for the data shortage for multiple types of data shortages, the user can easily know what kind of data shortage is in what range of the explanatory variables. can.

Further, as shown in FIG. 14, the output unit 3 has the explanatory variables and the objective variables as axes, respectively, and plots data arranged in a map divided into a plurality of regions by the range of the explanatory variables and the range of the objective variables. Display the figure. At that time, the output unit 3 displays and outputs the area where there is a lack of data in the plot diagram separately from other areas, but when the output request range is notified, it is included in the output request range in the plot diagram and the data is output. The area where there is a shortage of is displayed and output separately from other areas.

In this way, since the data shortage is superimposed and displayed on the data plot diagram, the user can visually recognize the data shortage area together with the data. Further, by superimposing and displaying the data shortage of the output request range on the data plot diagram, the user can visually recognize the data shortage area of the output request range together with the data.

FIG. 15 is a diagram showing an example of a screen displayed and output by the output unit 3 shown in FIG.

When the presence or absence of data shortage is determined for each data area group by the above-mentioned data amount shortage area determination process 201, data area low accuracy determination process 202, and input / output high sensitivity area determination process 203, as shown in FIG. The output unit 3 displays and outputs the number of data shortage areas 1401 which is the number of areas where it is determined that there is insufficient data for each range of the objective variables for each of the plurality of explanatory variables. In addition, when an explanatory variable is specified, the shortage reason 1402 that associates the range determined to have data shortage with the reason for the lack of data in that range is displayed for each area group of the explanatory variable. Output.

In this way, by displaying the number of data shortages for each range of the objective variables for multiple explanatory variables, the state of data shortage of each explanatory variable is compared and data is added in which range of which explanatory variable. It can be used to consider what to do.

FIG. 16 is a diagram showing an example of a screen displayed and output by the output unit 3 shown in FIG.

When the presence or absence of data shortage is determined for each data area group by the above-mentioned data amount shortage area determination process 201, data area low accuracy determination process 202, and input / output high sensitivity area determination process 203, as shown in FIG. The output unit 3 displays and outputs, for each of the plurality of explanatory variables, the data shortage area number history 1501 which is the history of the number of areas where it is determined that there is insufficient data for each range of the objective variable. In addition, when an explanatory variable is specified, the shortage reason 1502 that associates the range determined to have data shortage with the reason for the lack of data in that range is displayed and output for each region group of the explanatory variable. However, in this shortage reason 1502, it is determined that the data is insufficient in the previous judgment, and in the area where it is resolved in the current judgment, the reason for the previous data shortage is displayed together with the cancellation line. become.

In this way, by displaying the history of the number of areas where it is judged that there is insufficient data, it is possible to grasp the acquisition status of the data points of which range of data was added from the previous judgment to the current judgment. be able to.

FIG. 17 is a diagram showing an example of a screen displayed and output by the output unit 3 shown in FIG.

As shown in FIG. 17, as a result of determining whether or not there is a data shortage for each data area group by the above-mentioned data amount insufficient area determination process 201, data area low accuracy determination process 202, and input / output high sensitivity area determination process 203. If there is an explanatory variable for which it is determined that there is no data shortage for a certain area, the data shortage area for another explanatory variable and the reason 1601 are not presented.

As described above, in this embodiment, the data area of the data is divided into a plurality of areas, the index value is calculated for each area or each area group, and the presence or absence of data shortage is determined for each range of explanatory variables based on the index value. Since the judgment is made, it is possible to quantitatively judge the range of the values of the explanatory variables for which the data is insufficient and present the judgment result. As a result, it is possible to reduce the trouble of reconstructing the estimation model and the prediction model.

As described above, the output unit 3 not only presents the judgment result of the data shortage range calculation unit 2, but also transmits and outputs the judgment result of the data shortage range calculation unit 2 to the connected external device. It is also possible. In that case, if the external device is, for example, a controller, the explanatory variable of the data is the control parameter, and the objective variable is the stability of the control, the control parameter is set in the area where the data is insufficient as an unstable control area. It is also possible to avoid.

Hereinafter, the series of processes described above will be described with reference to specific data examples.

For example, suppose that there is data in which the air conditioning data of an air conditioner and the comfort level felt by a person are linked. The explanatory variables are the set temperature, the distance from the air conditioner, the temperature, the humidity, and the objective variable for this is the comfort level. The comfort level is, for example, 5 levels, 1. Dissatisfaction, 2. A little dissatisfied, 3. Normally, 4. Relatively satisfied, 5. It shall be expressed as very satisfying.

First, in step 608 of FIG. 12, the output unit 3 selects, for example, one set temperature from the explanatory variables.

Further, as shown in FIG. 3, the data shortage range calculation unit 2 divides the temperature-settable range into every two degrees and the comfort level into one step. Then, the data shortage range calculation unit 2 determines whether the data density is above a certain level for each divided range of the set temperature as shown in FIG. 6, and determines whether the data deviation is below a certain level as shown in FIG. judge. Further, in the data shortage range calculation unit 2, when interpolation is performed by data approximation in a region where the set temperature is 20 to 22 degrees and 22 to 24 degrees, the change in comfort level is 1 → with respect to the change in the set temperature. If the change is large as 4, it is judged as high sensitivity. The data shortage range calculation unit 2 similarly performs the above-mentioned data amount shortage, low accuracy, and high sensitivity determination processing for the other explanatory variables. As a result, if there is nothing that can explain the large change in the set temperature, a recommended area for observing the data in detail is determined between the areas determined to have high sensitivity. For example, a request is made to increase the data points of the set temperature of 22 degrees in the area of each degree, which is a region obtained by further dividing the area of every 2 degrees, and the output unit 3 displays and outputs to that effect. Further, after the data is replenished, the output unit 3 displays and outputs whether or not the high-sensitivity region has been eliminated. However, when the output request range is, for example, the comfort level 5, the high-sensitivity region is not included, so the above-mentioned processing is not performed.

In addition, as a presentation effect of these data shortage areas, if the data area is judged to have low accuracy in any explanatory variable, add another explanatory variable such as illuminance and observe whether the data shortage judgment is made. become. It should be noted that this observation is the progress of the history of the judgment result with the previous data, and if it is judged that there is no shortage, it can be explained by the newly added explanatory variable.

1 ... Data, 2 ... Data shortage range calculation unit, 3 ... Output unit, 4 ... Output range request unit, 103 ... Data shortage area judgment processing, 201 ... Data amount shortage area judgment processing, 202 ... Data area low accuracy judgment processing, 203 ... Input / output high-sensitivity area determination processing

Claims (12)

It is a data deficiency presentation system that presents whether or not there is a deficiency of data necessary for estimating the objective variable from the explanatory variables.
The data area of the data given as a combination of the value of the explanatory variable and the value of the objective variable is divided into a plurality of areas defined by the value of the explanatory variable and the value of the objective variable, and each of the divided areas or Data for calculating at least one index value related to data shortage for each region group in which the value of the explanatory variable is within a predetermined range, and determining whether or not there is a data shortage for each range of the explanatory variable based on the index value. Insufficient range calculation unit and
An output unit that outputs the judgment result of the data shortage range calculation unit, and
Data deficient presentation system. The data shortage range calculation unit calculates the density of data in the region group as the index value, and when the density is equal to or less than a predetermined threshold value, the data amount is insufficient in the range of the explanatory variables corresponding to the region group. Judging that there is a lack of data by judging that it is
The data shortage presentation system according to claim 1. The data shortage range calculation unit calculates a deviation of the amount of data between regions in the region group as the index value, and when the deviation is equal to or more than a predetermined threshold value, it is within the range of the explanatory variables corresponding to the region group. Judging that there is a lack of data by judging that the accuracy of the data is low,
The data shortage presentation system according to claim 1. The data shortage range calculation unit calculates the ratio of the change amount of the objective variable to the change amount of the explanatory variable of the data in the region group as the index value, and when the ratio is equal to or more than a predetermined threshold value, the region group is assigned. It is judged that there is a lack of data by judging that the sensitivity of the data is high within the range of the corresponding explanatory variables.
The data shortage presentation system according to claim 1. When the data shortage range calculation unit determines that the sensitivity of the data is high in the range of the explanatory variables corresponding to the region group, the data shortage range calculation unit acquires data in order to make the ratio less than the threshold value in the range of the explanatory variables. Calculate the power area and
The output unit displays and outputs the calculated area.
The data shortage presentation system according to claim 4. The data shortage range calculation unit determines whether or not there is a shortage of a plurality of types of data for different reasons based on a plurality of index values.
The output unit displays and outputs a range of explanatory variables having a lack of data in association with the reason for the lack of data in the range.
The data shortage presentation system according to claim 1. The output unit further displays a plot diagram in which the data is arranged on a map divided into a plurality of areas by the range of the explanatory variables and the range of the objective variables, with the explanatory variables and the objective variables as axes, respectively. Display and output the area where there is a lack of data in the plot diagram separately from other areas.
The data shortage presentation system according to claim 6. It also has an output range request unit that accepts the range of the objective variable to be predicted as the output request range and notifies the output unit.
When the output request range is notified, the output unit displays and outputs an area included in the output request range and lacking data in the plot diagram separately from other areas.
The data shortage presentation system according to claim 7. When there are a plurality of the explanatory variables, the output unit displays and outputs the number of regions for which it is determined that there is insufficient data for each range of the objective variables for each of the plurality of explanatory variables.
The data shortage presentation system according to claim 1. The output unit displays and outputs a history of the number of areas where it is determined that the data is insufficient.
The data shortage presentation system according to claim 9. The output unit displays and outputs the determination result of the data shortage range calculation unit, and also transmits and outputs it to a connected external device.
The data shortage presentation system according to claim 1. It is a data deficiency presentation method that presents whether or not there is a deficiency of data necessary for estimating the objective variable from the explanatory variables.
Information processing equipment
The data area of the data given as a combination of the value of the explanatory variable and the value of the objective variable is divided into a plurality of areas defined by the value of the explanatory variable and the value of the objective variable, and each of the divided areas or Data for calculating at least one index value related to data shortage for each region group in which the value of the explanatory variable is within a predetermined range, and determining whether or not there is a data shortage for each range of the explanatory variable based on the index value. Insufficient range calculation processing and
Output processing that outputs the judgment result of the data shortage range calculation processing, and
How to present the lack of data to execute .

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