Performance Evaluation of Long Short-Term Memory and Autoregressive Integrated Moving Average Time Series Models for Stock Price Prediction

Stock price prediction is a critical task in financial markets, often complicated by the challenges of modeling highly volatile, non-linear, and dynamic time series data. This study evaluates the performance of two prominent forecasting models: Long Short-Term Memory (LSTM) networks, known for their ability to capture long-term dependencies and non-linear patterns, and the Auto-Regressive Integrated Moving Average (ARIMA), a traditional statistical model adept at linear trend modeling. Historical stock price data from the Nigerian Exchange Limited (NGX) was utilized. Both models were implemented in a Python environment, and their predictive accuracy was assessed using performance metrics, including Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and R-squared (R²). Additionally, the Diebold-Mariano (DM) test was employed to statistically compare the models’ predictive accuracies. This study highlights the potential of LSTM models for robust stock price forecasting and provides valuable insights for selecting predictive models based on data complexity and market dynamics.