Advanced Stock Market Forecasting: A Comparative Analysis of ARIMA-GARCH, LSTM, and Integrated Wavelet-LSTM Models

Abstract

In the era of big data, accurate forecasting of corporate data is crucial for formulating effective strategies and decisions. This paper focuses on the prediction of key corporate indicators, taking TSLA, JD, MSFT, and TCEHY as case studies. It explores the application of three forecasting models: ARIMA-GARCH, LSTM, and Wavelet-LSTM. By comparing the predictive accuracy of these models, we find that each model has its strengths and weaknesses under different data characteristics. The study not only emphasizes the importance of accurate forecasting for corporate management and market prediction but also summarizes the adaptability and limitations of different models in dealing with complex time series data, providing valuable reference and insights for similar forecasting tasks.