Salīdzināt metodes

	Nestacionārs Transformer	Paplašinātais Dīkija-Fullera (ADF) vienības saknes tests	Informer
Nozare	Dziļā mācīšanās	Ekonometrija	Dziļā mācīšanās
Saime	Machine learning	Regression model	Machine learning
Izcelsmes gads	2022	1979	2021
Autors	Yong Liu et al.	David A. Dickey & Wayne A. Fuller	Zhou, H. et al.
Tips	Transformer-based time-series forecasting model	Unit-root test for stationarity	Transformer (ProbSparse self-attention)
Pirmavots	Liu, Y., Wu, H., Wang, J., & Long, M. (2022). Non-stationary transformers: Exploring the stationarity in time series forecasting. NeurIPS.	Dickey, D. A., & Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association, 74(366a), 427–431.	Zhou, H. et al. (2021). Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting. AAAI.
Citi nosaukumi	NS-Transformer, Non-stationary Transformer Network, Stationarization-based Transformer, Durağan-Olmayan Transformer	ADF test, Dickey-Fuller test, unit root test, Genişletilmiş Dickey-Fuller testi	Informer — Uzun Dizi Transformer Tahmini, Informer transformer, ProbSparse attention forecaster
Saistītās	3	4	5
Kopsavilkums	Non-stationary Transformer is a Transformer-based time-series forecasting architecture introduced by Yong Liu, Haixu Wu, Jianmin Wang, and Mingsheng Long at NeurIPS 2022. It addresses a fundamental tension in applying Transformers to real-world time series: over-stationarization during preprocessing strips out non-stationary signals that carry predictive information, while raw non-stationary inputs cause attention to collapse. The model resolves this through series stationarization paired with a novel de-stationary attention mechanism that restores the original temporal distribution in predictions.	The Augmented Dickey-Fuller (ADF) test is the most widely used test for a unit root — that is, for whether a time series is non-stationary and must be differenced before modelling. Introduced by David Dickey and Wayne Fuller in 1979 and extended by Said and Dickey in 1984 to series with higher-order autocorrelation, it regresses the change in the series on its lagged level plus lagged differences and asks whether the lagged-level coefficient is zero.	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.