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| Autoformer: 장기 시계열 예측을 위한 분해 트랜스포머× | ETS: 오차, 추세, 계절성 지수평활법× | |
|---|---|---|
| 분야≠ | 딥러닝 | 계량경제학 |
| 계열≠ | Machine learning | Regression model |
| 기원 연도≠ | 2021 | 2008 |
| 창시자≠ | Haixu Wu et al. (Tsinghua) | Hyndman, Koehler, Ord & Snyder (state space framework) |
| 유형≠ | Decomposition-based deep forecasting model | Exponential smoothing state space model |
| 원전≠ | Wu, H., Xu, J., Wang, J., & Long, M. (2021). Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting. NeurIPS, 34. link ↗ | Hyndman, R. J., Koehler, A. B., Ord, J. K. & Snyder, R. D. (2008). Forecasting with Exponential Smoothing: The State Space Approach. Springer. DOI ↗ |
| 별칭 | 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 |
| 관련≠ | 4 | 5 |
| 요약≠ | 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. |
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