System, method, and computer program product for forecasting weather-based demand using proxy data

• US 7,844,517 B2
• Filed: 11/12/2003
• Issued: 11/30/2010
• Est. Priority Date: 01/18/1996
• Status: Expired due to Fees

• Current Assignee: Planalytics, Inc.
• Original Assignee: Planalytics, Inc.
• Inventors: Walsh, Paul E., Kirk, William E., Miller, Eli H., Willen, Michael A., Estornell, David G.
• Primary Examiner(s): Dixon; Thomas
• Assistant Examiner(s): Vizvary; Gerald C
• Application Number:
  US10/705,758
• Publication Number:
  US 20040225556A1
• Time in Patent Office: 2,575 Days
• Field of Search: None
• US Class Current: 705/35
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• 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
• CPC Class Codes: G06Q 40/00 Finance; Insurance; Tax strategies; Processing of corporate or income taxes
• CPC Class Codes: G06Q 40/04 Trading; Exchange, e.g. stocks, commodities, derivatives or currency exchange

Abstract

Weather metrics data and a weather factor relationship knowledgebase are received and used to forecast the weather-based demand. The weather factor relationship knowledgebase can be a weather-impact model. The weather-impact model can comprise at least one of an empirical scoring matrix, a weather indices template, and a proxy model conditions template. The weather-impact model can be derived from an analysis of normalized proxy sales history data. Optionally, the weather-based demand can be scaled, deaggregated, or both.

Claims

1. A system for forecasting weather-based demand, comprising:

a recombination processor wherein;

said recombination processor is configured to receive weather metrics data;

said recombination processor is configured to receive weather factor relationship weather factor relationship data from a weather factor relationship database, wherein the weather factor relationship data from the weather factor relationship database is different from the weather metrics data; and

said recombination processor is configured to produce normalized weather factor metrics data based on the weather metrics data and the weather factor relationship the weather factor relationship data from the weather factor relationship database, the normalized weather factor metrics data being indicative of a percentage increase or decrease in a demand relationship value in a first time period over a second time period.

2. The system of claim 1, wherein said weather factor relationship database is a weather-impact model.

3. The system of claim 2, wherein said weather-impact model comprises at least one of an empirical scoring matrix, a weather indices template, and a proxy model conditions template.

4. The system of claim 2, wherein said weather-impact model is derived from an analysis of normalized proxy sales history data.

5. The system of claim 4, wherein said normalized proxy sales history data are derived from at least one of previously stored sales history data for a product from an entity, sales history data for said product from a second entity, sales history data for said product from a source external to the system, sales history data for a category that includes said product, and sales history data for a proxy product, the proxy product having a similar weather-based demand relationship as said product.

6. The system of claim 1, further comprising a volatility scaling processor wherein:

said volatility scaling processor is different from said recombination processor;

said volatility scaling processor is configured to receive said normalized weather factor metric data;

said volatility scaling processor is configured to receive volatility scale factor data; and

said volatility scaling processor is configured to produce scaled weather factor metric data.

7. The system of claim 6, further comprising a deaggregation processor wherein:

said deaggregation processor is configured to receive said scaled weather factor metric data;

said deaggregation processor is configured to receive deaggregation data; and

said deaggregation processor is configured to produce deaggregated weather factor metric data.

8. The system of claim 1, further comprising a deaggregation processor wherein:

said deaggregation processor is different from said recombination processor;

said deaggregation processor is configured to receive said normalized weather factor metric data;

said deaggregation processor is configured to receive deaggregation data; and

said deaggregation processor is configured to produce deaggregated weather factor metric data.

9. A method for forecasting weather-based demand, comprising:

receiving by a processor, weather metrics data;

receiving, by the processor, weather factor relationship weather factor relationship data from a weather factor relationship database, wherein the weather factor relationship database is different from the weather metric data; and

forecasting by the processor, the weather-based demand by using normalized weather factor metrics data based on the weather metrics data and the weather factor relationship weather factor relationship data from the weather factor relationship database, the normalized weather factor metrics data being indicative of a percentage increase or decrease in a demand relationship value in a first time period over a second time period.

10. The method of claim 9, wherein the weather factor relationship database is a weather-impact model.

11. The method of claim 10, wherein the weather-impact model comprises at least one of an empirical scoring matrix, a weather indices template, and a proxy model conditions template.

12. The method of claim 10, wherein the weather-impact model is derived from an analysis of normalized proxy sales history data.

13. The method of claim 9, further comprising:

scaling the weather-based demand.

14. The method of claim 9, further comprising:

deaggregating the weather-based demand.

15. A computer program product for forecasting weather-based demand, said computer program product having computer program code means stored on a non-transitory storage medium, said computer program code means comprising:

a first program code means for causing a processor to receive weather metrics data;

a second program code means for causing the processor to receive weather factor relationship weather factor relationship data from a weather factor relationship database, wherein the weather factor relationship database is different from the weather metric data; and

a third program code means for causing the processor to forecast the weather-based demand by using normalized weather factor metrics data based on the weather metrics data and the weather factor relationship weather factor relationship data from the weather factor relationship database, the normalized weather factor metrics data being indicative of a percentage increase or decrease in a demand relationship value in a first time period over a second time period.

16. The computer program product of claim 15, wherein the weather factor relationship database is a weather-impact model.

17. The computer program product of claim 16, wherein the weather-impact model comprises at least one of an empirical scoring matrix, a weather indices template, and a proxy model conditions template.

18. The computer program product of claim 16, wherein the weather-impact model is derived from an analysis of normalized proxy sales history data.

19. The computer program product of claim 15, further comprising:

a fourth program code means for causing the processor to scale the weather-based demand.

20. The computer program product of claim 15, further comprising:

a fourth program code means for causing the processor to deaggregate the weather-based demand.

21. A non-transitory storage medium having instructions stored thereon, the instructions comprising:

instructions for receiving weather metrics data;

instructions for receiving weather factor relationship data from a weather factor relationship database; and

instructions for forecasting weather-based demand by using normalized weather factor metrics data based on the weather metrics data and the weather factor relationship data from the weather factor relationship database, the normalized weather factor metrics data being indicative of a percentage increase or decrease in a demand relationship value in a first time period over a second time period.