مقایسهٔ روش‌ها

	Informer	مدل آریما (میانگین متحرک یکپارچه خودرگرسیو)	پچ‌تی‌اس‌تی
حوزه	یادگیری عمیق	اقتصادسنجی	یادگیری عمیق
خانواده	Machine learning	Regression model	Machine learning
سال پیدایش	2021	2015	2023
پدیدآور	Zhou, H. et al.	Box & Jenkins (Box-Jenkins methodology)	Nie, Y. et al.
نوع	Transformer (ProbSparse self-attention)	Univariate time-series model	Transformer for time series forecasting
منبع بنیادین	Zhou, H. et al. (2021). Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting. AAAI.	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	Informer — Uzun Dizi Transformer Tahmini, Informer transformer, ProbSparse attention forecaster	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	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.	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.