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Enhancing Short-Term Electrical Load Forecasting Using SARIMA, XGBoost, LSTM, and VMD-Based Signal Decomposition
A framework for a comparative evaluation of four representative forecasting methods: the Seasonal Autoregressive Integrated Moving Average (SARIMA) model, Extreme Gradient Boosting, the Long Short-Term Memory (LSTM) neural network, and a hybrid Variational Mode Decomposition–LSTM (VMD-LSTM) model is proposed.