System of Sequential Kernel Regression Modeling for Forecasting Financial Data

• US 20130031019A1
• Filed: 10/05/2012
• Published: 01/31/2013
• Est. Priority Date: 07/19/2011
• Status: Active Grant

• Current Assignee: GE Intelligent Platforms Incorporated (Emerson Electric Company)
• Original Assignee: SmartSignal Corporation (Emerson Electric Company)
• Inventors: Herzog, James Paul
• Granted Patent: US 9,235,208 B2
• Time in Patent Office: Days
• US Class Current: 705/348
• CPC Class Codes: G05B 23/0243 model based detection method, e.g. first-principles knowledge model
• CPC Class Codes: G06Q 30/0202 Market predictions or forecasting for commercial activities

Abstract

A monitoring system for determining the future behavior of a financial system includes an empirical model to receive reference data that indicates the normal behavior of the system and input pattern arrays. Each input pattern array has a plurality of input vectors, while each input vector represents a time point and has input values representing a plurality of parameters indicating the current condition of the system. The model generates estimate values based on a calculation that uses an input pattern array and the reference data to determine a similarity measure between the input values and reference data. The estimate values, in the form of an estimate matrix, include at least one estimate vector of inferred estimate values, and represents at least one time point that is not represented by the input vectors. The inferred estimate values are used to determine a future behavior of the financial system.

Claims

1. A monitoring system for determining the future behavior of a financial system, comprising:

an empirical model module configured to;

receive reference data that indicates the normal behavior of the system,

receive input pattern arrays, each input pattern array having a plurality of input vectors, each input vector representing a time point and having input values representing a plurality of parameters indicating the current behavior of the system, and

generate estimate values based on a calculation that uses an input pattern array and the reference data to determine a similarity measure between the input values and reference data, wherein the estimate values are in the form of an estimate matrix that includes at least one estimate vector of inferred estimate values, each estimate matrix representing at least one time point that is not represented by the input vectors; and

a forecasting module configured to use the inferred estimate values to determine a future condition of the system.

2. The system of claim 1 wherein the empirical module is configured to generate weight values by using the similarity measures, and uses the weight values in a calculation with the reference data to generate the estimate matrix.

3. The system of claim 1 wherein the forecasting module is configured to use the most recent estimate matrix to update the estimate values for use to determine the future behavior of the system.

4. The system of claim 1 wherein the forecasting module is configured to provide values for a single estimate vector to represent a single time point across multiple estimate matrices.

5. The system of claim 1 wherein the forecasting module is configured to provide values for a single estimate vector to represent each estimate matrix.

6. The system of claim 1 wherein the forecasting module is configured to form a trend line for at least one parameter represented by the inferred estimate values to indicate the expected behavior of the object.

7. The system of claim 6 wherein the forecasting module is configured to form a new trend line with each new estimate matrix.

8. The system of claim 6 wherein the forecasting module is configured to form boundary trend lines to define a range of expected behavior of the object.

9. The system of claim 7 wherein the forecasting module is configured to form an upper boundary trend line with maximum estimate values from the time points, and a lower boundary trend line with minimum estimate values from the time points.

10. A method for determining the future behavior of a financial system, the method comprising:

receiving reference data that indicates the normal behavior of the financial system;

receiving input pattern arrays, each input pattern array having a plurality of input vectors, each input vector representing a time point and having input values representing a plurality of parameters indicating the current behavior of the system;

generating estimate values based on a calculation that uses an input pattern array and the reference data to determine a similarity measure between the input values and reference data, wherein the estimate values are in the form of an estimate matrix that includes at least one estimate vector of inferred estimate values, each estimate matrix representing at least one time point that is not represented by the input vectors; and

determining a future condition of the financial system using the inferred estimate values.

11. The method of claim 10 further comprising generating weight values by using the similarity measures, and using the weight values in a calculation with the reference data to generate the estimate matrix.

12. The method of claim 10 further comprising updating the estimate values for use to determine the future behavior of the system according to the most recent estimate matrix.

13. The method of claim 10 wherein the forecasting module is configured to provide values for a single estimate vector to represent a single time point across multiple estimate matrices.

14. The method of claim 10 further comprising providing values for a single estimate vector to represent each estimate matrix.

15. The method of claim 10 further comprising forming a trend line for at least one parameter represented by the inferred estimate values to indicate the expected behavior of the object.

16. The method of claim 15 further comprising forming a new trend line with each new estimate matrix.

17. The method of claim 15 further comprising forming boundary trend lines to define a range of expected behavior of the object.

18. The method of claim 16 further comprising forming an upper boundary trend line with maximum estimate values from the time points, and a lower boundary trend line with minimum estimate values from the time points.

19. An apparatus for determining the future behavior of a financial system, the apparatus comprising:

an interface with an input and an output, the input configured to receive reference data that indicates the normal behavior of the financial system, the input further configured to receive input pattern arrays, each input pattern array having a plurality of input vectors, each input vector representing a time point and having input values representing a plurality of parameters indicating the current behavior of the system; and

at least one processor coupled to the interface, the at least one processor being configured to generate estimate values based on a calculation that uses an input pattern array and the reference data to determine a similarity measure between the input values and reference data, wherein the estimate values are in the form of an estimate matrix that includes at least one estimate vector of inferred estimate values, each estimate matrix representing at least one time point that is not represented by the input vectors, the processor configured to determine a future condition of the system using the inferred estimate values and provide the future condition at the output.