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Hourly load forecasting via PatchTST and Temporal Fusion Transformers: comparative system analysis with CatBoost and N-HiTS network models A.E. Dzgoev, E.V. Klimkin, V.V. Chernyauskas, A.V. Brailovsky, R.N. Rezenkov Abstract. Under modern conditions of dynamic demand behavior and external factors, traditional statistical autoregressive modeling methods (ARIMA, SARIMAX) are increasingly outperformed by machine learning algorithms (Gradient Boosting) […]https://izvestiyakbncran.ru/wp-content/uploads/2026/03/image.png8028