השוואת שיטות

	Autoformer: טרנספורמר מבוסס פירוק לחיזוי סדרות עתיות ארוכות טווח	ETS: שגיאה, מגמה, החלקה אקספוננציאלית עונתית
תחום	למידה עמוקה	אקונומטריקה
משפחה	Machine learning	Regression model
שנת המקור	2021	2008
הוגה השיטה	Haixu Wu et al. (Tsinghua)	Hyndman, Koehler, Ord & Snyder (state space framework)
סוג	Decomposition-based deep forecasting model	Exponential smoothing state space model
מקור מכונן	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.
כינויים	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
קשורות	4	5
תקציר	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.