قارن الطرق

	Temporal Fusion Transformer	نموذج ARIMA (الانحدار الذاتي المتكامل للمتوسط المتحرك)	المُخبِر (Informer)
المجال	التعلم العميق	الاقتصاد القياسي	التعلم العميق
العائلة	Machine learning	Regression model	Machine learning
سنة النشأة	2021	2015	2021
صاحب الطريقة	Lim, B., Arık, S. Ö., Loeff, N. & Pfister, T.	Box & Jenkins (Box-Jenkins methodology)	Zhou, H. et al.
النوع	Attention-based deep learning forecasting architecture	Univariate time-series model	Transformer (ProbSparse self-attention)
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	Zhou, H. et al. (2021). Informer: Beyond Efficient Transformer for Long Sequence 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	Informer — Uzun Dizi Transformer Tahmini, Informer transformer, ProbSparse attention forecaster
ذات صلة	6	5	5
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).	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.