مقایسهٔ روش‌ها

	Informer	مدل آریما (میانگین متحرک یکپارچه خودرگرسیو)	N-HiTS	پچ‌تی‌اس‌تی
حوزه	یادگیری عمیق	اقتصادسنجی	یادگیری عمیق	یادگیری عمیق
خانواده	Machine learning	Regression model	Machine learning	Machine learning
سال پیدایش	2021	2015	2023	2023
پدیدآور	Zhou, H. et al.	Box & Jenkins (Box-Jenkins methodology)	Challu, C. et al.	Nie, Y. et al.
نوع	Transformer (ProbSparse self-attention)	Univariate time-series model	Deep neural forecasting (hierarchical interpolation)	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	Challu, C. et al. (2023). NHITS: Neural Hierarchical Interpolation for Time Series Forecasting. AAAI.	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	N-HiTS — Hiyerarşik İnterpolasyon Tahmini, NHITS, Neural Hierarchical Interpolation	PatchTST — Yama Tabanlı Zaman Serisi Transformer, patch-based time series transformer, channel-independent transformer
مرتبط	5	5	3	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).	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.	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.