System and method for large-scale automatic forecasting

• US 20030200134A1
• Filed: 03/28/2003
• Published: 10/23/2003
• Est. Priority Date: 03/29/2002
• Status: Abandoned Application

• Current Assignee: SAS Institute Incorporated
• Original Assignee: SAS Institute Incorporated
• Inventors: Elsheimer, David Bruce, Leonard, Michael James
• Application Number:
  US10/402,849
• Publication Number:
  US 20030200134A1
• Time in Patent Office: Days
• US Class Current: 705/10
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0202 Market predictions or forecasting for commercial activities

Abstract

A computer-implemented method and system for large-scale automatic forecasting. The method and system determine which forecasting models in a pool of forecasting models may best predict input transactional data. Candidate models are selected from the pool of forecasting models by comparing characteristics of the models in the pool with characteristics of the input transaction data. To further reduce the number of models, hold-out sample analysis is performed for the candidate models. The candidate model(s) that best perform with respect to the hold-out sample analysis are used to generate forecasted output.

Claims

1. A computer-implemented method for automatically selecting forecasting models, comprising the steps of:

receiving a pool of forecasting models, wherein the forecasting models in the pool have at least one pre-identified model characteristic;

receiving time series data indicative of transactional activity;

determining at least one statistical characteristic of the time series data;

comparing the determined statistical characteristic of the time series data with the pre-identified model characteristic of the forecasting models in the pool to identify candidate forecasting models;

determining a data subset from the time series data and a hold-out sample from the time series data;

optimizing at least one parameter of the candidate forecasting models using the time series data subset;

calculating statistics-of-fit for the candidate forecasting models using the hold-out sample; and

selecting at least one of the candidate forecasting models based upon the calculated statistics-of-fit of the candidate forecasting models.

2. The method of claim 1, wherein the time series data includes data representative of operating a physical system over a period of time.

3. The method of claim 2, wherein the physical system is a manufacturing system.

4. The method of claim 2, wherein the physical system is a system selected from the group consisting of a grocery store chain, a retail store chain, and combinations thereof.

5. The method of claim 1, further comprising the step of:

optimizing at least one parameter of the selected candidate forecasting model using substantially all of the time series data.

6. The method of claim 1, further comprising the step of:

generating a forecasted output using the selected candidate forecasting model.

7. The method of claim 6, wherein the forecasted output includes a prediction, an upper confidence limit, and a lower confidence limit.

8. The method of claim 1, wherein the time series data is accumulated from a file of transactional data using at least one pre-selected parameter.

9. The method of claim 8, wherein the pre-selected parameters include an accumulation frequency, a seasonal cycle, and an accumulation method.

10. The method of claim 8, wherein the file of transactional data is accumulated from an Internet website.

11. The method of claim 8, wherein the file of transactional data is accumulated from a point-of-sale (POS) device.

12. The method of claim 1, further comprising the step of:

generating a forecasted output for one time period using the selected candidate forecasting model;

receiving additional time series data for the one time period; and

calculating at least one in-sample statistic-of-fit for the selected candidate forecasting model using the forecasted output and the additional time series data.

13. The method of claim 12, further comprising the step of:

generating an evaluation output that indicates the in-sample statistic-of-fit.

14. The method of claim 13, further comprising the step of:

selecting a new candidate forecasting model from the pool of forecasting models based on the evaluation output.

15. The method of claim 1, further comprising the step of:

generating a forecasted output for a plurality of time periods using the selected candidate forecasting model;

receiving additional time series data for the plurality of time periods; and

calculating at least one in-sample statistic-of-fit for the selected candidate forecasting model using the forecasted output and the additional time series data.

16. The method of claim 15, further comprising the step of:

generating a performance analysis output that indicates the in-sample statistic-of-fit.

17. The method of claim 16, further comprising the step of:

selecting a new candidate forecasting model from the pool of forecasting models based on the performance analysis output.

18. The method of claim 1, further comprising the step of:

receiving special event information,

wherein the special event information is incorporated into a candidate forecasting model.

19. The method of claim 18, wherein the special event information includes at least one special event that occurs during a calendar year and causes deviations to occur within the time series data.

20. The method of claim 19, wherein the special event occur at a specified time and endures for a specified time period.

21. The method of claim 1, wherein the time series data includes historical data about one or more past promotions, wherein a candidate forecasting model is selected after taking into account intervention factors.

22. The method of claim 21, wherein the intervention analysis is used to assess one or more past promotions.

23. An automatic forecasting system, comprising:

a pool of forecasting models, wherein each forecasting model has at least one pre-identified model characteristic;

a file containing time series data indicative of transactional activity;

a forecasting model selection module that receives the file of time series data and selects at least one forecasting model from the pool of forecasting models by determining at least one statistical characteristic of the time series data and comparing the statistical characteristic with the pre-identified model characteristic of the forecasting models in the pool; and

a forecasting module coupled to the forecasting model selection module that fits the selected forecasting model to the time series data and generates a forecasted output.

24. The automatic forecasting system of claim 23, wherein the file of time series data comprises time series data that has been accumulated from a file of transactional data.

25. The automatic forecasting system of claim 23, wherein the forecasting model selection module comprises:

a diagnostic module that receives a file of time series data, and that determines the statistical characteristic of the time series data and compares the statistical characteristic with the pre-identified model characteristic of each forecasting model to determine candidate forecasting models; and

a selector module coupled to the diagnostic module that calculates a statistic-of fit for each candidate forecasting model, and compares the statistics-of-fit to select the forecasting model.

26. The automatic forecasting system of claim 25, wherein the statistic-of-fit calculated by the selector module is of a type selected by a system user as a model selection criterion.

27. The automatic forecasting system of claim 25, wherein the selector module further comprising:

a candidate optimizer module coupled to the diagnostic module that optimizes at least one parameter of the candidate forecasting models using a subset of the time series data; and

a hold-out forecasting module coupled to the candidate optimizer module that calculates a statistic-of-fit for each candidate forecasting model, and that selects the candidate forecasting model based on the statistics-of-fit.

28. The automatic forecasting system of claim 23, wherein the forecasted output includes one-step ahead forecast data, and further comprising:

an evaluation module that receives the one-step ahead forecast data from the forecasting module and receives a file of actual data corresponding to the one-step ahead forecast data, and that is configured to calculate a statistic-of-fit from the one-step ahead forecast data and the file of actual data.

29. The automatic forecasting system of claim 23, wherein the forecasted output includes h-step ahead forecast data, and further comprising:

a performance analysis module that receives the h-step ahead forecast data from the forecasting module and receives a file of actual data corresponding to the h-step ahead forecast data, and that is configured to calculate a statistic-of-fit from the h-step ahead forecast data and the file of actual data.

30. The automatic forecasting system of claim 23, wherein special event information is incorporated into the selected forecasting model.

31. The automatic forecasting system of claim 30, wherein the special event information includes at least one special event that occurs during a calendar year and causes deviations to occur within the time series data.

32. The automatic forecasting system of claim 31, wherein the special event occur at a specified time and endures for a specified time period.

33. The automatic forecasting system of claim 23, wherein the time series data includes historical data about one or more past promotions, wherein a forecasting model is selected after taking into account intervention factors.

34. The automatic forecasting system of claim 33, wherein the intervention analysis is used to assess one or more past promotions.

35. A computer-implemented apparatus for automatically selecting forecasting models, comprising:

means for receiving a pool of forecasting models, wherein the forecasting models in the pool have at least one pre-identified model characteristic;

means for receiving time series data indicative of millions of transactional activities;

means for determining at least one statistical characteristic of the time series data;

means for comparing the determined statistical characteristic of the time series data with the pre-identified model characteristic of the forecasting models in the pool to identify candidate forecasting models;

means for determining a data subset from the time series data and a hold-out sample from the time series data;

means for optimizing for each time series at least one parameter of the candidate forecasting models using the time series data subset;

means for calculating statistics-of-fit for the candidate forecasting models using the hold-out sample; and

means for selecting at least one of the candidate forecasting models based upon the calculated statistics-of-fit of the candidate forecasting models.