Comparative Analysis of Stock Price Prediction Based on ARIMA and LSTM Models

This study delves into the application of the Autoregressive Integrated Moving Average Model (ARIMA), Long Short-Term Memory Network (LSTM), and their hybrid models of stock price prediction. It offers a comparative analysis of these models’ performances, highlighting the significance of accurate forecasting in the volatile world of stock markets. The paper begins by underscoring the importance of stock market forecasting and the limitations of traditional time series analysis techniques, particularly the ARIMA model’s struggle with long-term dependencies and nonlinear features. It then explores the LSTM model’s prowess in capturing long-term patterns within time series data and the potential of hybrid models that marry the strengths of both LSTM and ARIMA to enhance forecasting precision. Through case analysis, the research evaluates these models’ performance across different market environments, culminating in comprehensive recommendations for model selection. The study concludes that hybrid models effectively integrate the strengths of ARIMA and LSTM, significantly boosting the accuracy and robustness of time series forecasting. It advocates for future research to explore a range of datasets to affirm the model’s universality and robustness and to probe the impact of varying data characteristics on predictive power, thereby offering more targeted guidance for practical applications.