Comparative Analysis of Traditional and Deep Models for Short-Term Forecasting of Price Time Series with the Inclusion of Exogenous Factors

This study presents a hybrid price time series forecasting system based on four models: XGBoost, LSTM, Prophet, and ARIMA. The system integrates temperature data as an exogenous regressor to improve predictive accuracy. A graphical environment for data loading, model training, and results visualization is implemented. Metrics such as MAE, MSE, MAPE, WAPE, and Pearson coefficient are used to evaluate the effectiveness. Additionally, SHAP analysis interprets the importance of input features. The results show that combining classical and deep models increases the robustness and precision of forecasts.