Salīdzināt metodes

	ETSformer: Eksponenciālās izlīdzināšanas transformeri laika sēriju prognozēšanai	ETS: Kļūda, tendence, sezonas eksponenciālā izlīdzināšana
Nozare	Dziļā mācīšanās	Ekonometrija
Saime	Machine learning	Regression model
Izcelsmes gads	2022	2008
Autors	Gerald Woo et al.	Hyndman, Koehler, Ord & Snyder (state space framework)
Tips	Hybrid decomposition-based Transformer architecture	Exponential smoothing state space model
Pirmavots	Woo, G., Liu, C., Sahoo, D., Kumar, A., & Hoi, S. (2022). ETSformer: Exponential smoothing transformers for time-series forecasting. arXiv preprint.	Hyndman, R. J., Koehler, A. B., Ord, J. K. & Snyder, R. D. (2008). Forecasting with Exponential Smoothing: The State Space Approach. Springer.
Citi nosaukumi	Exponential Smoothing Transformer, ETS Transformer, ETSformer forecasting model, Üstel Düzleştirme Transformatörü	exponential smoothing state space model, innovations state space model, Holt-Winters family, ETS — Hata/Trend/Mevsimsellik Üstel Düzleştirme
Saistītās	2	5
Kopsavilkums	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.	ETS is a comprehensive exponential smoothing framework that automatically selects additive or multiplicative combinations of the error (E), trend (T) and seasonal (S) components of a time series. Formalised as an innovations state space model by Hyndman, Koehler, Ord and Snyder in 2008, it unifies and generalises the Holt-Winters family of forecasting methods.