Methoden vergleichen

	ETSformer: Exponential Smoothing Transformer für Zeitreihenprognosen	Autoformer: Decomposition Transformer für die Langzeit-Zeitreihenprognose
Fachgebiet	Deep Learning	Deep Learning
Familie	Machine learning	Machine learning
Entstehungsjahr	2022	2021
Urheber	Gerald Woo et al.	Haixu Wu et al. (Tsinghua)
Typ	Hybrid decomposition-based Transformer architecture	Decomposition-based deep forecasting model
Wegweisende Quelle	Woo, G., Liu, C., Sahoo, D., Kumar, A., & Hoi, S. (2022). ETSformer: Exponential smoothing transformers for time-series forecasting. arXiv preprint.	Wu, H., Xu, J., Wang, J., & Long, M. (2021). Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting. NeurIPS, 34.
Aliasnamen	Exponential Smoothing Transformer, ETS Transformer, ETSformer forecasting model, Üstel Düzleştirme Transformatörü	Auto-Correlation Transformer, Decomposition Transformer, Series Decomposition Forecaster, Oto-Korelasyon Ayrışım Transformer
Verwandt	2	4
Zusammenfassung	ETSformer is a deep learning architecture for time-series forecasting introduced by Woo et al. in 2022. It integrates classical exponential smoothing principles directly into the Transformer framework by replacing standard self-attention with an exponential smoothing attention mechanism. The model decomposes a time series into level, growth (trend), and seasonal components, allowing it to leverage both the long-range dependency modeling of Transformers and the interpretable structure of statistical ETS models.	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.