Salīdzināt metodes

	ETSformer: Eksponenciālās izlīdzināšanas transformeri laika sēriju prognozēšanai	Autoformer: Transformer ar dekompozīciju ilgtermiņa laika virkņu prognozēšanai	ETS: Kļūda, tendence, sezonas eksponenciālā izlīdzināšana
Nozare	Dziļā mācīšanās	Dziļā mācīšanās	Ekonometrija
Saime	Machine learning	Machine learning	Regression model
Izcelsmes gads	2022	2021	2008
Autors	Gerald Woo et al.	Haixu Wu et al. (Tsinghua)	Hyndman, Koehler, Ord & Snyder (state space framework)
Tips	Hybrid decomposition-based Transformer architecture	Decomposition-based deep forecasting model	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.	Wu, H., Xu, J., Wang, J., & Long, M. (2021). Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting. NeurIPS, 34.	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	Auto-Correlation Transformer, Decomposition Transformer, Series Decomposition Forecaster, Oto-Korelasyon Ayrışım Transformer	exponential smoothing state space model, innovations state space model, Holt-Winters family, ETS — Hata/Trend/Mevsimsellik Üstel Düzleştirme
Saistītās	2	4	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.	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.	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.