قارن الطرق

	Temporal Fusion Transformer	نموذج ARIMA (الانحدار الذاتي المتكامل للمتوسط المتحرك)	DeepAR	N-HiTS
المجال	التعلم العميق	الاقتصاد القياسي	التعلم العميق	التعلم العميق
العائلة	Machine learning	Regression model	Machine learning	Machine learning
سنة النشأة	2021	2015	2020	2023
صاحب الطريقة	Lim, B., Arık, S. Ö., Loeff, N. & Pfister, T.	Box & Jenkins (Box-Jenkins methodology)	Salinas, D., Flunkert, V. & Gasthaus, J. (Amazon)	Challu, C. et al.
النوع	Attention-based deep learning forecasting architecture	Univariate time-series model	Autoregressive recurrent neural network (probabilistic forecasting)	Deep neural forecasting (hierarchical interpolation)
المصدر التأسيسي	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.	Box, G. E. P., Jenkins, G. M., Reinsel, G. C. & Ljung, G. M. (2015). Time Series Analysis: Forecasting and Control (5th ed.). Wiley. ISBN: 978-1118675021	Salinas, D., Flunkert, V., Gasthaus, J. & Januschowski, T. (2020). DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks. International Journal of Forecasting, 36(3), 1181–1191.	Challu, C. et al. (2023). NHITS: Neural Hierarchical Interpolation for Time Series Forecasting. AAAI.
الأسماء البديلة	Temporal Fusion Transformer (TFT), TFT, interpretable multi-horizon forecasting transformer	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli	DeepAR — Olasılıksal RNN Tahmini, probabilistic autoregressive RNN forecasting, Amazon DeepAR	N-HiTS — Hiyerarşik İnterpolasyon Tahmini, NHITS, Neural Hierarchical Interpolation
ذات صلة	6	5	5	3
الملخص	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.	ARIMA is a univariate time-series forecasting model that combines autoregressive, integrated (differencing), and moving-average components to predict a single continuous series from its own past. It is the centrepiece of the Box-Jenkins methodology set out in Box, Jenkins, Reinsel & Ljung's Time Series Analysis (5th ed., 2015).	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.	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.