﻿ 基于小波回声状态网络的时间序列预测

# 基于小波回声状态网络的时间序列预测Prediction of Time Series Based on Wavelet Echo State Network

Abstract: In order to forecast time series with multi-scale characteristics better, the echo state network model is combined with wavelet analysis method to create a new prediction model which is wavelet echo state network. The original time series are processed with the Mallat algorithm and Dau-bechies wavelet based on wavelet multi-scale analysis theory that can respectively get the details of the different layers and overview part of sequences. Then different echo state network forecasting models are respectively created based on the different sequence characteristics to forecast and eventually add the forecast data of detail sequences and overview sequence to obtain the original time series prediction result. The simulation examples of forecast model for a country gross national product show that this model can well fit the development trend of time series and the forecast accuracy is higher.

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