A Hybrid SARIMAX–LSTM Framework for Predicting Price Volatility in High-Tech Digital Markets: Evidence from NVIDIA

This study develops a Hybrid SARIMAX–LSTM model to improve the accuracy and robustness of stock price forecasting in digital and volatile financial markets. The model combines the linear and seasonal forecasting strengths of the Seasonal Autoregressive Integrated Moving Average with Exogenous Variables (SARIMAX) with the nonlinear learning capability of the Long Short-Term Memory (LSTM) network. Using historical data from NVDIA Corporation, the hybrid framework was optimized through smart weighting to balance the contribution of both components. The results show that the model achieved a Root Mean Square Error (RMSE) of 8.59 and a coefficient of determination (R²) of 0.9166, indicating that over 91 percent of price variance was accurately explained. Residual analysis confirmed unbiased predictions with normally distributed errors, demonstrating high stability and adaptability under volatile market conditions. Compared with individual models, the hybrid approach produced smoother and more consistent forecasts. Overall, the Hybrid SARIMAX–LSTM framework offers an interpretable and reliable tool for digital market forecasting and AI-based financial decision-making.