Financial Time Series Forecasting Based on LSTM Neural Network optimized by Wavelet Denoising and Whale Optimization Algorithm

Abstract

In order to further explore the application of deep learning in predicting financial market time series data and improve the accuracy of the prediction, this paper adopts a financial time series prediction method based on wavelet denoising, whale optimization algorithm and long-short term memory (LSTM) neural network. This article chooses 10 common evaluation indexes in the financial market as the input, the financial time series data are denoised by wavelet analysis. Then the optimal LSTM neural network parameters are obtained by whale optimization algorithm (WOA). Finally, the LSTM neural network algorithm is used for stock prediction to output the predicted closing price. To verify the effectiveness of WP-WOA-LSTM model, three other neural networks are used to compare with the forecasting result. By comparing the prediction accuracy of different methods, it is obvious that the mean absolute error (MAE) of LSTM neural network under whale optimization algorithm can be reduced by 22 % compared with the standard LSTM neural network. Therefore, the results show that WOA-LSTM model has significantly improved the prediction accuracy.