Methoden vergleichen

	Augmented-Dickey-Fuller (ADF)-Test auf Einheitswurzel	Autoformer: Decomposition Transformer für die Langzeit-Zeitreihenprognose
Fachgebiet	Ökonometrie	Deep Learning
Familie	Regression model	Machine learning
Entstehungsjahr	1979	2021
Urheber	David A. Dickey & Wayne A. Fuller	Haixu Wu et al. (Tsinghua)
Typ	Unit-root test for stationarity	Decomposition-based deep forecasting model
Wegweisende Quelle	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.	Wu, H., Xu, J., Wang, J., & Long, M. (2021). Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting. NeurIPS, 34.
Aliasnamen	ADF test, Dickey-Fuller test, unit root test, Genişletilmiş Dickey-Fuller testi	Auto-Correlation Transformer, Decomposition Transformer, Series Decomposition Forecaster, Oto-Korelasyon Ayrışım Transformer
Verwandt	4	4
Zusammenfassung	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.	Autoformer is a deep learning architecture for long-term time-series forecasting, introduced by Wu et al. from Tsinghua University at NeurIPS 2021. It replaces the standard self-attention mechanism with an Auto-Correlation mechanism that exploits periodic dependencies in the frequency domain, and embeds a progressive series decomposition block throughout the encoder and decoder to separately model trend and seasonal components.