مقایسهٔ روش‌ها

	DeepAR	مدل آریما (میانگین متحرک یکپارچه خودرگرسیو)	پچ‌تی‌اس‌تی
حوزه	یادگیری عمیق	اقتصادسنجی	یادگیری عمیق
خانواده	Machine learning	Regression model	Machine learning
سال پیدایش	2020	2015	2023
پدیدآور	Salinas, D., Flunkert, V. & Gasthaus, J. (Amazon)	Box & Jenkins (Box-Jenkins methodology)	Nie, Y. et al.
نوع	Autoregressive recurrent neural network (probabilistic forecasting)	Univariate time-series model	Transformer for time series forecasting
منبع بنیادین	Salinas, D., Flunkert, V., Gasthaus, J. & Januschowski, T. (2020). DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks. International Journal of Forecasting, 36(3), 1181–1191.	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	Nie, Y., Nguyen, N. H., Sinthong, P. & Kalagnanam, J. (2023). A Time Series is Worth 64 Words: Long-term Forecasting with Transformers. ICLR.
نام‌های دیگر	DeepAR — Olasılıksal RNN Tahmini, probabilistic autoregressive RNN forecasting, Amazon DeepAR	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli	PatchTST — Yama Tabanlı Zaman Serisi Transformer, patch-based time series transformer, channel-independent transformer
مرتبط	5	5	3
خلاصه	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.	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).	PatchTST is a patch-based Transformer architecture for time series forecasting, introduced by Nie and colleagues in 2023, that cuts each series into overlapping patches treated as tokens and processes channels independently. It balances computational efficiency with strong accuracy on long-horizon forecasting.