Hydrological time series prediction method based on multiple-factor wavelet neural network model

Abstract

The invention discloses a hydrological time series prediction method based on a multiple-factor wavelet neural network model. The invention provides a multiple-factor wavelet neural network model used for predicting the hydrological time sequence. The model takes a multiple-time sequence message as input, and the multiple time sequence message not only comprises the current wavelet coefficient of a prediction target time sequence but also comprises the current wavelet coefficient of other time sequences relevant to the time sequence; mutual information between the multiple-time sequence message and the prediction target time sequence serves as a measurement for judging the relevance of the multiple-time sequence message and the prediction target time sequence; other time sequences of strong relevance are selected; and a wavelet function selection criteria based on the a coefficient of weighted correlation is further utilized to select the optimal wavelet function for the model. Compared with the prior art, the method disclosed by the invention has the advantages of higher prediction accuracy and better expandability and practical value.

Description

technical field [0001] The invention relates to a complex time series forecasting method, in particular to a hydrological time series forecasting method based on a multi-factor wavelet neural network model, belonging to the technical field of hydrological forecasting. Background technique [0002] Time series data mining research mainly includes: prediction, classification, similarity search and sequence pattern mining, and complex time series prediction is one of the challenging problems in the field of data mining. A better method to solve complex time series forecasting problems is the time series forecasting method based on wavelet neural network model. [0003] Wavelet analysis is a milestone development in the history of Fourier analysis. It has the advantages of simultaneous localization of time and frequency, so it is known as the "microscope" of mathematics. Compared with Fourier analysis, which can only provide frequency domain representation, wavelet transform ca...

AI Technical Summary

Problems solved by technology

[0007] The technical problem to be solved by the present invention is to overcome the disadvantages of using a single time series as the input of the wavelet neural network model for hydrological time series prediction in the prior art, and to provide a hydrological time series prediction method based on a multi-factor wavelet neural network model

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