BILSTM Prediction of Financial Time Series Based on Wavelet and Attention Mechanism
To improve the prediction accuracy of the financial series,taking the closing price of Shanghai Compos-ite Index as an example,a new prediction model of financial time series was constructed by combining wavelet analysis with BiLSTM and Attention mechanism.Wavelet analysis can remove the impurities in the time series,the BiLSTM network can repair the omission of context information,and the attention mechanism can select the key points.By comparing with the prediction effect of LSTM and other models without wavelet analysis,the result show that the neu-ral network model composed of BiLSTM and Attention has good prediction accuracy for financial time series data and can effectively predict the trend of financial time series.
Wavelet decompositionNeural networkBidirectional short-term and long-term memory neural net-work
NETL
NSTL
万方数据
