방법 비교

	Temporal Fusion Transformer	DeepAR
분야	딥러닝	딥러닝
계열	Machine learning	Machine learning
기원 연도	2021	2020
창시자	Lim, B., Arık, S. Ö., Loeff, N. & Pfister, T.	Salinas, D., Flunkert, V. & Gasthaus, J. (Amazon)
유형	Attention-based deep learning forecasting architecture	Autoregressive recurrent neural network (probabilistic forecasting)
원전	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.	Salinas, D., Flunkert, V., Gasthaus, J. & Januschowski, T. (2020). DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks. International Journal of Forecasting, 36(3), 1181–1191.
별칭	Temporal Fusion Transformer (TFT), TFT, interpretable multi-horizon forecasting transformer	DeepAR — Olasılıksal RNN Tahmini, probabilistic autoregressive RNN forecasting, Amazon DeepAR
관련	6	5
요약	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.	DeepAR is Amazon's industrial forecasting model, introduced by Salinas, Flunkert and Gasthaus (2017; published 2020), that uses an autoregressive recurrent neural network to estimate the parameters of a probability distribution at each step, producing a confidence interval rather than a single point forecast. It can model many related time series jointly within one model.