Neural network system for time series data prediction

• US 20110066579A1
• Filed: 08/27/2010
• Published: 03/17/2011
• Est. Priority Date: 09/16/2009
• Status: Abandoned Application

• Current Assignee: OKI Electric Industry Company Limited
• Original Assignee: OKI Electric Industry Company Limited
• Inventors: Ikada, Satoshi
• Application Number:
  US12/805,999
• Publication Number:
  US 20110066579A1
• Time in Patent Office: Days
• US Class Current: 706/21
• CPC Class Codes: G06N 3/02 Neural networks

Abstract

A neural network system for predicting time series data. The system receives analyzed data obtained by multiresolution analysis of the time series data. The input processing layer of the system includes a series of neurons corresponding to the different levels of analysis, each neuron receiving the analyzed data for it own level. The output of each of these neurons is supplied as an additional input to the neuron for the next lower level of analysis. A predicted value is derived from the output of the neuron at the lowest level. The passing of results from one level to another improves prediction accuracy and simplifies the structure of any further processing layers.

Claims

1. A neural network system for processing time series data to calculate predicted time series values, comprising:

an input unit for receiving analyzed data obtained by multiresolution analysis of the time series data, the analyzed data including data for different levels of analysis, from a highest level of analysis to a lowest level of analysis, the different levels of analysis indicating different frequency characteristics of the time series data; and

a processing unit including an input processing layer for generating first output data by operating on the analyzed data for the highest level of analysis to obtain an output value for the highest level of analysis, then obtaining an output value for each level of analysis in a descending series of levels by operating, at each level of analysis in the descending series of levels, on the analyzed data of the each level of analysis and the output value obtained at the next higher level of analysis in the descending series of levels;

the predicted time series values being derived from the first output data.

2. The neural network system of claim 1, wherein higher levels of analysis represent lower frequency characteristics of the time series data.

3. The neural network system of claim 1, wherein the first output data are derived from the output value obtained at a lowest level of analysis in the descending series of levels.

4. The neural network system of claim 1, wherein the descending series of levels include all the levels of analysis from the highest level of analysis to the lowest level of analysis.

5. The neural network system of claim 1, wherein the input processing layer includes one neuron for each level of analysis.

6. The neural network system of claim 5, wherein:

the one neuron for the highest level of analysis calculates a weighted sum of the analyzed data for the highest level of analysis; and

the one neuron for each level of analysis below the highest level of analysis in the descending series of levels calculates a weighted sum of the analyzed data for the each level of analysis below the highest level of analysis and the output value obtained from the next higher level of analysis in the descending series of levels.

7. The neural network system of claim 6, wherein the one neuron for each level of analysis obtains the output value for the each level of analysis by applying a transfer function to the weighted sum.

8. The neural network system of claim 1, wherein the processing unit further includes at least one delay processing unit for receiving the analyzed data at different times, storing the analyzed data received over a predetermined most recent interval of time, and supplying the stored analyzed data to the input processing layer.

9. The neural network system of claim 1, wherein the processing unit further includes:

an intermediate processing layer for generating second output data by operating on the first output data; and

an output processing layer for generating third output data by operating on second output data, the predicted time series values being derived from the third output data.

10. The neural network system of claim 9, wherein the intermediate processing layer includes a single neuron that takes a weighted sum of a predetermined quantity of most recent first output data obtained from the input processing layer.

11. The neural network system of claim 10, further comprising a delay unit for storing the predetermined quantity of most recent first output data.

12. The neural network system of claim 1, wherein the multiresolution analysis is a wavelet-based frequency analysis, in which the level of analysis increases as the analyzed data are obtained from lower-frequency wavelets.

13. The neural network system of claim 12, wherein the analyzed data include wavelet coefficients.

14. The neural network system of claim 13, wherein:

the input unit also receives scaling coefficient data as analyzed data at the highest level of analysis; and

the processing unit operates on a combination of the wavelet coefficient data at the highest level of analysis and the scaling coefficient data.

15. The neural network system of claim 1, wherein:

the input unit also receives correlated data correlated with the analyzed data; and

the input processing layer also operates on a combination of the output value obtained at the lowest level of analysis in the descending series and the correlated data.

16. The neural network system of claim 1, wherein the neural network system is trained by back propagation.