Linganisha mbinu

	Temporal Fusion Transformer	DeepAR
Nyanja	Ujifunzaji wa Kina	Ujifunzaji wa Kina
Familia	Machine learning	Machine learning
Mwaka wa asili	2021	2020
Mwanzilishi	Lim, B., Arık, S. Ö., Loeff, N. & Pfister, T.	Salinas, D., Flunkert, V. & Gasthaus, J. (Amazon)
Aina	Attention-based deep learning forecasting architecture	Autoregressive recurrent neural network (probabilistic forecasting)
Chanzo asilia	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.
Majina mbadala	Temporal Fusion Transformer (TFT), TFT, interpretable multi-horizon forecasting transformer	DeepAR — Olasılıksal RNN Tahmini, probabilistic autoregressive RNN forecasting, Amazon DeepAR
Zinazohusiana	6	5
Muhtasari	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.