In recent years, deep learning (DL) models have shown remarkable potential in the field of forecasting. Models such as DeepAR, neuralProphet , PatchTST, Temporal Fusion Transformer, NHiTS, and TiDE have achieved state-of-the-art performance on traditional academic benchmarks. However, despite their promise, these models often fall short when applied to the complexities of real-world forecasting.
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