ARIMA-LSTM Hybrid Models: A Comparative Analysis for Forecasting International Oil Prices and Stock Market Indices

This study aims to compare forecasting analysis based on ARIMA-LSTM hybrid models. ARIMA is a classic time series analysis method, while LSTM is a deep learning model based on neural networks. This research employed both models to predict the future trends of international oil prices and stock market indices. The results show that the ARIMA-LSTM hybrid model outperforms using ARIMA or LSTM models alone in terms of prediction accuracy. Moreover, the ARIMA-LSTM hybrid model also exhibits better robustness and stability. Therefore, this study recommends using the ARIMA-LSTM hybrid model for time series forecasting analysis in practical applications.