Linganisha mbinu

	Non-stationary Transformer	Kipimo cha Mizizi-Mmoja cha Augmented Dickey-Fuller (ADF)
Nyanja	Ujifunzaji wa Kina	Ekonometriki
Familia	Machine learning	Regression model
Mwaka wa asili	2022	1979
Mwanzilishi	Yong Liu et al.	David A. Dickey & Wayne A. Fuller
Aina	Transformer-based time-series forecasting model	Unit-root test for stationarity
Chanzo asilia	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.
Majina mbadala	NS-Transformer, Non-stationary Transformer Network, Stationarization-based Transformer, Durağan-Olmayan Transformer	ADF test, Dickey-Fuller test, unit root test, Genişletilmiş Dickey-Fuller testi
Zinazohusiana	3	4
Muhtasari	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.