CALCULATING MACHINE, PREDICTION METHOD, AND PREDICTION PROGRAM

• US 20140351008A1
• Filed: 05/19/2014
• Published: 11/27/2014
• Est. Priority Date: 05/27/2013
• Status: Abandoned Application

• Current Assignee: Hitachi, Ltd.
• Original Assignee: Hitachi, Ltd.
• Inventors: OYAMATSU, MASAYUKI, HIROI, Kazushige
• Application Number:
  US14/281,266
• Publication Number:
  US 20140351008A1
• Time in Patent Office: Days
• US Class Current: 705/7.29
• CPC Class Codes: G06Q 30/0202 Market predictions or forecasting for commercial activities
• CPC Class Codes: G06Q 50/01 Social networking

Abstract

A calculating machine stores intermediate data generated for each product based on social media data including statements on a plurality of products. The intermediate data about each of the products includes at least a frequency of statements on each of the products for a predetermined period of time. The products include a first product that is not displayed for provision to a consumer at a present time, or at least a second product that has been displayed for provision at the present time. The calculating machine stores sales amount data indicating a sales amount of the second product, and calculates a social media correlation degree indicating a correlation between the intermediate data about the first product and the intermediate data about the second product to predict a sales amount of the first product based on the calculated social media correlation degree and the sales amount data about the second product.

Claims

1. A calculating machine, comprising:

a processor; and

a memory,

wherein the calculating machine stores intermediate data generated for each of a plurality of products or services based on social media data including statements on the products or services for a predetermined period of time in the memory,

the intermediate data includes at least a frequency of statements on each of the products or services for the predetermined period of time,

the products or services include a first product or service that is not displayed for provision to a consumer at a present time, and at least a second product or service that has been displayed for provision at the present time, and

the calculating machine stores sales amount data indicating a sales amount of the second product or service in the memory, and includes a correlation degree calculation unit configured to calculate a social media correlation degree indicating a correlation between the intermediate data about the first product or service and the intermediate data about the second product or service, and a demand prediction unit configured to predict a sales amount of the first product or service based on the calculated social media correlation degree and the sales amount data about the second product or service.

2. The calculating machine according to claim 1,

wherein the calculating machine stores a release time indicating a time when release of each of the products or services is started or a time when release of each of the products or services has been started in the memory, and

the correlation degree calculation unit finds a relationship between a release time of the first product or service and the present time for the first product or service, finds a release time of the second product or service and a reference time having a relationship identical to the found relationship for the second product or service, and extracts the intermediate data about the second product or service before the found reference time in order to calculate the social media correlation degree from the extracted intermediate data about the second product or service and the intermediate data about the first product or service.

3. The calculating machine according to claim 1,

wherein the calculating machine stores product information indicating attribute of each of the products or services in the memory,

the correlation degree calculation unit calculates a distance indicating a difference between attribute of the first product or service and attribute of the second product or service based on the product information, and

the demand prediction unit predicts the sales amount of the first product or service based on the calculated social media correlation degree, the calculated distance, and the sales amount data of the second product or service.

4. The calculating machine according to claim 3, further comprising:

an input and output unit configured to receive an instruction from a user; and

a visualization unit configured to display an identifier of the second product or service and the predicted sales amount of the first product or service on the input and output unit,

wherein the visualization unit receives the identifier of the second product or service instructed by the user through the input and output unit, and

the correlation degree calculation unit calculates the social media correlation degree indicating a correlation between the intermediate data about the second product or service to which the identifier is instructed and the intermediate data about the first product or service.

5. The calculating machine according to claim 4,

wherein the calculating machine stores external factor data indicating a state by a predetermined period of time when each of the products or services has been provided,

the sales amount data indicates a sales amount of the second product or service by the predetermined period of time,

the calculating machine includes an external factor contribution degree calculation unit configured to analyze external factor data about the second product or service in a multiple regression analysis based on the external factor data about the second product or service and the sales amount data about the second product or service to calculate a regression coefficient of the external factor data about the second product or service in order to calculate a prediction sales amount using the calculated regression coefficient, and

the demand prediction unit predicts the sales amount of the first product or service based on the prediction sales amount calculated with the external factor contribution degree calculation unit, the social media correlation degree, and the calculated distance.

6. The calculating machine according to claim 5,

wherein the visualization unit displays an influence degree of the calculated regression coefficient and the predicted sales amount of the first product or service through the input and output device and receives the influence degree instructed by the user through the input and output device, and

the external factor contribution degree calculation unit calculates the prediction sales amount based on the instructed influence degree and the calculated regression coefficient.

7. The calculating machine according to claim 6, wherein the external factor contribution degree calculation unit updates the regression coefficient with a result obtained by multiplying the instructed influence degree by the calculated regression coefficient to calculate the prediction sales amount using the updated regression coefficient.

8. A prediction method using a calculating machine including a processor and a memory,

wherein the calculating machine stores intermediate data generated for each of a plurality of products or services based on social media data including statements on the products or services for a predetermined period of time in the memory,

the intermediate data includes at least a frequency of statements on each of the products or services for the predetermined period of time,

the products or services include a first product or service that is not displayed for provision to a consumer at a present time, and at least a second product or service that has been displayed for provision at the present time,

the calculating machine stores sales amount data indicating a sales amount of the second product or service in the memory,

the method comprising:

calculation of a correlation degree in which a social media correlation degree indicating a correlation between the intermediate data about the first product or service and the intermediate data about the second product or service is calculated by the processor; and

prediction of a demand in which a sales amount of the first product or service is predicted by the processor based on the calculated social media correlation degree and the sales amount data about the second product or service.

9. The prediction method according to claim 8,

wherein the calculating machine stores a release time indicating a time when release of each of the products or services is started or a time when release of each of the products or services has been started in the memory, and

the calculation of the correlation degree includes finding, by the processor, a relationship between a release time of the first product or service and the present time for the first product or service, finding, by the processor, a release time of the second product or service and a reference time having a relationship identical to the found relationship for the second product or service, extracting, by the processor, the intermediate data about the second product or service before the found reference time, and calculating, by the processor, the social media correlation degree from the extracted intermediate data about the second product or service and the intermediate data about the first product or service.

10. The prediction method according to claim 8,

wherein the calculating machine stores product information indicating attribute of each of the products or services in the memory,

the calculation of the correlation degree includes calculating, by the processor, a distance indicating a difference between attribute of the first product or service and attribute of the second product or service based on the product information, and

the prediction of the demand includes predicting, by the processor, the sales amount of the first product or service based on the calculated social media correlation degree, the calculated distance, and the sales amount data of the second product or service.

11. The prediction method according to claim 10,

wherein the calculating machine further includes an input and output unit configured to receive an instruction from a user,

the method includes visualization of displaying an identifier of the second product or service and the predicted sales amount of the first product or service on the input and output unit,

the visualization includes receiving, by the processor, the identifier of the second product or service instructed by the user through the input and output unit, and

the calculation of the correlation degree includes calculating, by the processor, the social media correlation degree indicating a correlation between the intermediate data about the second product or service to which the identifier is instructed and the intermediate data about the first product or service.

12. The prediction method according to claim 11,

wherein the calculating machine stores external factor data indicating a state by a predetermined period of time when each of the products or services has been provided,

the sales amount data indicates a sales amount of the second product or service by the predetermined period of time,

the method includes calculating an external factor contribution degree in which the processor analyzes external factor data about the second product or service in a multiple regression analysis based on the external factor data about the second product or service and the sales amount data about the second product or service to calculate a regression coefficient of the external factor data about the second product or service in order to calculate the prediction sales amount using the calculated regression coefficient, and

the prediction of the demand includes predicting, by the processor, the sales amount of the first product or service based on the prediction sales amount calculated in the calculation of the external factor contribution degree, the social media correlation degree, and the calculated distance.

13. The prediction method according to claim 12,

wherein the visualization includes displaying, by the processor, an influence degree of the calculated regression coefficient and the predicted sales amount of the first product or service through the input and output device and receiving, by the processor, the influence degree instructed by the user through the input and output device, and

the calculation of the external factor contribution degree includes calculating, by the processor, the prediction sales amount based on the instructed influence degree and the calculated regression coefficient.

14. The prediction method according to claim 13, wherein the calculation of the external factor contribution degree includes updating, by the processor, the regression coefficient with a result obtained by multiplying the instructed influence degree by the calculated regression coefficient, and calculating, by the processor, the prediction sales amount using the updated regression coefficient.

15. A prediction program for causing a calculating machine including a processor and a memory to perform processes,

wherein the calculating machine stores intermediate data generated for each of a plurality of products or services based on social media data including statements on the products or services for a predetermined period of time in the memory,

the intermediate data includes at least a frequency of statements on each of the products or services for the predetermined period of time,

the products or services include a first product or service that is not displayed for provision to a consumer at a present time, and at least a second product or service that has been displayed for provision at the present time,

the calculating machine stores sales amount data indicating a sales amount of the second product or service in the memory, and

the prediction program causing the calculating machine to perform the processes comprising:

calculation of a correlation degree in which a social media correlation degree indicating a correlation between the intermediate data about the first product or service and the intermediate data about the second product or service are calculated; and

prediction of a demand in which a sales amount of the first product or service is predicted based on the calculated social media correlation degree and the sales amount data about the second product or service.