Computer-implemented system and method for storing data analysis models

• US 8,014,983 B2
• Filed: 06/22/2010
• Issued: 09/06/2011
• Est. Priority Date: 05/09/2005
• Status: Active Grant

• Current Assignee: SAS Institute Incorporated
• Original Assignee: SAS Institute Incorporated
• Inventors: Crowe, Keith E., Leonard, Michael J.
• Primary Examiner(s): FREJD, RUSSELL WARREN
• Application Number:
  US12/820,440
• Publication Number:
  US 20100257133A1
• Time in Patent Office: 441 Days
• Field of Search: 703/2, 703/22, 702/181, 702/189, 705/10, 707/104.1
• US Class Current: 703/2
• CPC Class Codes: G06F 17/18 for evaluating statistical data , e.g. average values, frequency distributions, probability functions, regression analysis forecasting specially adapted for a specific administrative, business or logistic context G06Q10/04
• CPC Class Codes: G06Q 10/10 Office automation; Time management
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data
• CPC Class Codes: G06Q 30/0202 Market predictions or forecasting for commercial activities

Abstract

Computer-implemented systems and methods for processing time series data that is indicative of a data generation activity occurring over a period of time. A model specification hierarchical data structure is used for storing characteristics that define a time series model. A fitted model hierarchical data structure stores characteristics that define a fitted time series model. The characteristics of the fitted time series model are defined through an application of the time series model to the time series data.

Claims

1. A computer-implemented system for processing time series data that is indicative of a data generation activity occurring over a period of time, comprising:

one or more processors;

a computer-readable storage medium containing instructions configured to cause the one or more processors to perform operations including;

storing characteristics that define a time series model, wherein the characteristics of the time series model are stored in a model specification hierarchical data structure; and

storing characteristics that define a fitted time series model, wherein the characteristics of the fitted time series model are stored in a fitted model hierarchical data structure, and defined through an application of the time series model to the time series data.

2. The system of claim 1, wherein the time series model characteristics are stored in an Extensible Markup Language (XML) format, and wherein the fitted time series model characteristics are stored in an Extensible Markup Language (XML) format.

3. The system of claim 1, wherein the fitted time series model characteristics include parameters optimized to fit the time series data.

4. The system of claim 1, wherein the fitted time series model characteristics include characteristics associated with a forecast function that describes how to forecast the time series data.

5. The system of claim 1, further comprising instructions configured to cause the one or more processors to perform operations including:

using the stored fitted time series model characteristics to provide forecasts of time series data.

6. The system of claim 5, further comprising instructions configured to cause the one or more processors to perform operations including:

accessing the fitted time series model characteristics that are stored in the fitted model hierarchical data structure; and

generating a forecast based upon the accessed fitted time series model characteristics and upon future input values.

7. The system of claim 5, further comprising instructions configured to cause the one or more processors to perform operations including:

using the fitted time series model characteristics for goal seeking, optimization, scenario analysis, or control situations.

8. The system of claim 1, further comprising instructions configured to cause the one or more processors to perform operations including:

storing the time series model characteristics independent of storing of the time series data.

9. The system of claim 1, further comprising instructions configured to cause the one or more processors to perform operations including:

generating the fitted time series model characteristics within the fitted model hierarchical data structure.

10. The system of claim 1, wherein the fitted time series model is used to generate backcasts.

11. The system of claim 1, wherein the fitted time series model is used to generate time series components.

12. The system of claim 11, wherein the time series components include seasonal components and trend components.

13. The system of claim 1, further comprising instructions configured to cause the one or more processors to perform operations including:

selecting one or more time series models to model the time series data, wherein the one or more time series models are model specifications having a configuration for storing time series model characteristics in an Extensible Markup Language (XML) format.

14. The system of claim 1, further comprising instructions configured to cause the one or more processors to perform operations including:

generating a forecast function based upon a selected time series model, historical data, and estimated parameters, wherein the forecast function is used to provide forecast scoring.

15. A computer-implemented method for processing time series data that is indicative of a data generation activity occurring over a period of time, comprising:

storing, using one or more processors, characteristics that define a time series model, wherein the characteristics of the time series model are stored in a model specification hierarchical data structure; and

storing, using one or more processors, characteristics that define a fitted time series model, wherein the characteristics of the fitted time series model are stored in a fitted model hierarchical data structure, and defined through an application of the time series model to the time series data.

16. The method of claim 15, wherein the time series model characteristics are stored in an Extensible Markup Language (XML) format, and wherein the fitted time series model characteristics are stored in an Extensible Markup Language (XML) format.

17. The method of claim 15, further comprising:

using the stored fitted time series model characteristics to provide forecasts of time series data.

18. A computer program product for processing time series data, tangibly embodied in a machine-readable storage medium, including instructions configured to cause a data processing system to:

store characteristics that define a time series model, wherein the characteristics of the time series model are stored in a model specification hierarchical data structure; and

store characteristics that define a fitted time series model, wherein the characteristics of the fitted time series model are stored in a fitted model hierarchical data structure, and defined through an application of the time series model to the time series data.