Bandingkan kaedah

	Temporal Fusion Transformer	Informer	N-HiTS
Bidang	Pembelajaran Mendalam	Pembelajaran Mendalam	Pembelajaran Mendalam
Keluarga	Machine learning	Machine learning	Machine learning
Tahun asal	2021	2021	2023
Pengasas	Lim, B., Arık, S. Ö., Loeff, N. & Pfister, T.	Zhou, H. et al.	Challu, C. et al.
Jenis	Attention-based deep learning forecasting architecture	Transformer (ProbSparse self-attention)	Deep neural forecasting (hierarchical interpolation)
Sumber perintis	Lim, B., Arık, S. Ö., Loeff, N. & Pfister, T. (2021). Temporal Fusion Transformers for Interpretable Multi-Horizon Time Series Forecasting. International Journal of Forecasting, 37(4), 1748–1764.	Zhou, H. et al. (2021). Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting. AAAI.	Challu, C. et al. (2023). NHITS: Neural Hierarchical Interpolation for Time Series Forecasting. AAAI.
Alias	Temporal Fusion Transformer (TFT), TFT, interpretable multi-horizon forecasting transformer	Informer — Uzun Dizi Transformer Tahmini, Informer transformer, ProbSparse attention forecaster	N-HiTS — Hiyerarşik İnterpolasyon Tahmini, NHITS, Neural Hierarchical Interpolation
Berkaitan	6	5	3
Ringkasan	The Temporal Fusion Transformer (TFT), introduced by Lim, Arık, Loeff and Pfister in 2021, is an interpretable deep learning architecture for multi-horizon time series forecasting. It combines variable selection, gating, multi-horizon attention and quantile outputs, processing static, past and known-future inputs together to produce multi-step forecasts.	Informer is a Transformer-based model introduced by Zhou et al. in 2021 for long-sequence time-series forecasting, using a ProbSparse self-attention mechanism that lowers the computational complexity of the standard Transformer to O(L log L). It is built for problems that demand predictions across thousands of future steps.	N-HiTS (Neural Hierarchical Interpolation for Time Series Forecasting), introduced by Challu and colleagues in 2023, is a deep neural forecasting architecture that combines the hierarchical forecasts of multiple stacks operating at different sampling rates and merges them through interpolation. It extends N-BEATS to deliver markedly better accuracy on long forecast horizons.