基于经验模态分解和多分支LSTM网络汇率预测

doi:10.16088/j.issn.1001-6600.2020080201

Abstract

Abstract: As a new signal transformation algorithm, Empirical Mode Decomposition (EMD) can solve the limitation of some existing methods such as Fourier transform that are limited to specific basis functions. Aiming at the problem of insufficient prediction accuracy of artificial neural networks for high-frequency financial time series, this paper combines EMD and Weibull distribution to preprocess financial time series. A classification model based on EMD and multi-branch long short-term memory network is proposed in this paper. The multi-branch LSTM network based on EMD is used to extract information about price movements from high-frequency financial time series and make predictions about future price movements. By predicting the FX time series of EURUSD from 2009 to 2012, the experimental results show that the proposed model can obtain higher prediction accuracy and calculation speed. Compared with ordinary LSTM network, the generalization ability and model stability are improved.

Key words: LSTM network, financial time series, FX predicting, classification model, empirical mode decomposition, deep learning

CLC Number:

TP183

XUE Tao, QIU Senhui, LU Hao, QIN Xingsheng. Exchange Rate Prediction Based on Empirical Mode Decomposition and Multi-branch LSTM Network[J].Journal of Guangxi Teachers Education University (Philosophy and Social Sciences Edition), 2021, 39(2): 41-50.

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Exchange Rate Prediction Based on Empirical Mode Decomposition and Multi-branch LSTM Network

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