PREDICTION METHOD AND SYSTEM

• US 20080147486A1
• Filed: 12/18/2007
• Published: 06/19/2008
• Est. Priority Date: 12/18/2006
• Status: Abandoned Application

• Current Assignee: Lehigh University
• Original Assignee: Lehigh University
• Inventors: Wu, S. David
• Application Number:
  US11/958,964
• Publication Number:
  US 20080147486A1
• 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 30/0201 Market modelling; Market analysis; Collecting market data
• CPC Class Codes: G06Q 30/0202 Market predictions or forecasting for commercial activities

Abstract

A method and apparatus for predicting future data values based on past data values. Past data values such as past product demand values are received. Leading indicators are identified based on the past data values. The future data values are generated based on the leading indicators.

Claims

1. A method of identifying leading indicators comprising:

a. receiving a plurality of data streams;

b. selecting a cluster of the plurality of the received data streams;

c. determining a strength of a plurality of data streams of the cluster relative to at least a portion of the plurality of received data streams;

d. selecting at least one of the data streams having a strength exceeding a threshold value as a leading indicator.

2. The method of claim 1 where the strength of each data stream of the cluster is determined relative to the plurality of data streams excluding the data stream whose strength is being determined.

3. The method of claim 1 further comprising the step of repeating steps (b) and (c) for a different cluster of the plurality of the received data streams when the strength of the selected cluster does not exceed the threshold value.

4. The method of claim 1 where the strength of each data stream of the cluster is determined by computing a correlation between that data stream of the cluster subject to a time offset, and at least a portion of the plurality of received data streams.

5. The method of claim 1 where the strength of each data stream of the cluster is determined based on a correlation between data values of each data stream of the cluster subject to a time offset and at least a portion of the cluster of data streams.

6. The method of claim 1 wherein the plurality of received data streams are indexed by time.

7. The method of claim 1 wherein the data streams include demand information for a group of products and the method comprises identifying one or more products from the group of products as leading indicators for the group of products.

8. A method of generating predicted values of a plurality of data streams comprising:

a. identifying at least one leading indicator according to the method of claim 1; and

b. generating predicted values for at least one of the data streams based on the at least one of the data streams selected as leading indicators.

9. The method of claim 8 wherein the predicted values are generated by

regressing the plurality of data streams against the data streams of the at least one of the data streams selected as leading indicators to determine the corresponding regression parameters;

and

generating a prediction for the plurality of data streams using a function defined by the regression parameters.

10. The method of claim 8 comprising generating first predicted values using a first prediction method and adapting the first prediction method in response to the leading indicators.

11. The method of claim 10 wherein the first prediction method is adapted to improve the accuracy of its prediction.

12. The method of claim 8 wherein the strength of the plurality of data streams of the cluster is determined from a portion of such data streams from an estimation period (EP) and the method comprises validating each such data stream having a strength exceeding a threshold value based on a portion of such data stream from a validation period (VP).

13. The method of claim 8 wherein the data streams include demand information corresponding to short life-cycle products and the generated predicted values provide demand forecast information for the short life-cycle products.

14. A method of generating a prediction comprising:

a. receiving a plurality of data streams;

b. determining a strength of one or more of the plurality of data streams;

c. identifying at least one of the one or more data streams having a strength greater than a threshold value as a leading indicator;

d. generating predicted values for the plurality of data streams based on the at least one of the one or more data streams identified as a leading indicator.

15. The method of claim 14 step b comprises determining the strength of at least one cluster of the plurality of data streams, step c comprises identifying a cluster having a strength greater than a threshold value as a leading indicator, and step d comprises generating predicted values based on the cluster.

16. The method of claim 14 comprising determining strength by generating a correlation between the one or more of the plurality of data streams subject to a time offset and the plurality of received data streams excluding the one or more of the plurality of data streams.

17. The method of claim 14 wherein at least one of the plurality of data streams comprises a composite data stream.

18. The method of claim 14 wherein the data streams correspond to one of capacity, inventory and finances and the method comprises performing capacity planning, inventory forecasting, and financial forecasting, respectively, based on the predicted values.

19. A method of generating a prediction comprising:

a. receiving a plurality of data streams;

b. computing a correlation between each of the plurality of data streams and the other of the plurality of data streams;

c. selecting data streams responsive to the strength of their respective computed correlation;

d. generating predicted values for at least one of the plurality of data streams based on the selected data streams.

20. The method of claim 19 wherein at least one of the plurality of data streams comprises a composite data stream.

21. The method of claim 19 wherein the predicted values are generated by:

regressing the plurality of data streams against the selected data streams to determine the corresponding regression parameters; and

generating a prediction for the plurality of data streams using a function defined by the regression parameters.

23. The system of claim 21 wherein at least one of the plurality of data streams comprises a composite data stream.

24. The system of claim 21 wherein the processor is further configured to control the system to generate the predicted values by:

regressing the plurality of data streams against the selected data streams to determine the corresponding regression parameters; and

generating a prediction for the plurality of data streams using a function defined by the regression parameters.

22. A computerized system for generating a prediction comprising:

a computerized database stored on a computer memory medium; and

a processor in communication with the database and configured to control the system to;

receive a plurality of data streams,

determine a strength of one or more of the plurality of data streams,

identify at least one of the one or more data streams having a strength greater than a threshold value as a leading indicator, and

generate predicted values for the plurality of data streams based on the at least one of the one or more data streams identified as a leading indicator.