Application Of Long-Short Term Memory Based On Wavelet Analysis In Stock Index Forecasting

Nowadays,stock trading has entered into people's daily life and has had a huge impact in the field of economics and finance.However,due to the strong instability of the stock market,investors have to take huge risks in stock trading.Under this background,how to accurately predict the future trend of the stock market has become a topic that scholars and investors pay close attention to.Due to many factors such as high noise,non-linearity,and uncertainty of investors'psychological expectations,the stock market forecasting is often considered as one of the most challenging issues in time series forecasting.For stock market researchers in modern society,how to accurately predict the change in stock price trend is still an unsolved problem.In the past few decades,with the continuous development of machine learn-ing and deep learning,some related models have been widely used in financial markets,such as Artificial Neural Network(ANN)and Support Vector Machine Machine(SVM)have obtained higher accuracy in stock price research.As a deep learning model that mimics the structure and function of biological neural network,neural network has a strong ability to fit nonlinear data and can effec-tively process it.At the same time,neural network has strong robustness and memory capabilities and self-learning ability,these characteristics make neural network have a great advantage in the prediction of financial markets.Compared with the traditional machine learning model and the neural network model,the integrated model combining the neural network with other statistical models can deal with complex stock market data more successfully and obtain better perfor-mance.Considering the complexity and variability of financial time series,the integrated prediction model of neural network and financial statistical model is considered to be one of the most attractive research directions.Based on this,this paper establish a WA-LSTM model which is a combination of Wavelet Analysis(WA)and Long-Short Term Memory Network(LSTM)to an-alyze and predict the stock index price.Wavelet Analysis can efectively separate the low-frequency information and high-frequency information in the stocks price time series,and can better deal with the volatility of the data.Long-Short Term Memory can not only inherit the advantages of traditional Recurrent Neural Net-works in time series and directed cycles,but also effectively avoid the problems of gradient disappearance and gradient explosion in traditional Recurrent Neural Networks.Combining the advantages of these two models,we can get a more accurate prediction model of the stock index prices'future trend.The main structure of this paper is as follows.First,we give a brief description of the concepts and related theoretical studies of Wavelet Analysis,Long-Short Term Memory,and Attention Mechanism.Secondly,we introduce the modeling process and parameter processing.Thirdly,select the price of Shanghai Stock Index as the original time series data and preprocess it.Then,use wavelet de-composition and reconstruction to decompose the original data into low-frequency subsequences and high-frequency subsequences,and train each subsequence by L-STM model,and the integrated model training result is the stock price prediction result.Then we can obtain the final prediction results and carry out error analy-sis.Finally,relate the existing stock index prediction model are compared under different evaluation criteria.From the comparison results,it can be analyzed that WA-LSTM model has certain superiority in fitting accuracy and prediction accuracy,and under a stable stock market,it has certain guiding significance for investors' investment behavior.At the end of the paper,we prospect the future development direction of the proposed model and explore the optimization of the model in depth.