Linganisha mbinu

	ETSformer: Transfoma za Ulaini wa Kielelezo kwa Utabiri wa Mfululizo wa Muda	Autoformer: Transformer ya Uharibifu kwa Utabiri wa Milipuko ya Muda Mrefu	ETS: Hitilafu, Mwenendo, Usanifu wa Majira wa Kielektroniki
Nyanja	Ujifunzaji wa Kina	Ujifunzaji wa Kina	Ekonometriki
Familia	Machine learning	Machine learning	Regression model
Mwaka wa asili	2022	2021	2008
Mwanzilishi	Gerald Woo et al.	Haixu Wu et al. (Tsinghua)	Hyndman, Koehler, Ord & Snyder (state space framework)
Aina	Hybrid decomposition-based Transformer architecture	Decomposition-based deep forecasting model	Exponential smoothing state space model
Chanzo asilia	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.
Majina mbadala	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
Zinazohusiana	2	4	5
Muhtasari	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.