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

A hydrological time series, wavelet neural network technology, applied in biological neural network models, special data processing applications, instruments, etc., to achieve high prediction accuracy, good scalability and practical value.

Inactive Publication Date: 2012-01-18
HOHAI UNIV
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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 m

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

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Embodiment Construction

[0029] The technical scheme of the present invention is described in detail below in conjunction with accompanying drawing:

[0030] The multi-factor wavelet neural network forecasting model of the present invention takes multi-time series information as input, not only includes the current wavelet coefficients of the forecast target time series, but also includes the current wavelet coefficients of other time series related to this time series, and its structure is as follows figure 1 As shown, the site to be predicted is A, and the sites related to A are B to I. This multi-factor wavelet neural network prediction model can also be called the river channel wavelet network model, abbreviated as RWNN.

[0031] The hydrological time series prediction method based on the multi-factor wavelet neural network model of the present invention is carried out according to the following steps:

[0032] Step 10, determining the relevant input time series;

[0033] In information theory, ...

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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...

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Application Information

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IPC IPC(8): G06F19/00G06N3/02
Inventor 朱跃龙李士进王继民范青松冯钧万定生
Owner HOHAI UNIV
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