Machine learningTime-series forecasting
ETSformer：用于时间序列预测的指数平滑Transformer模型
ETSformer是由Woo等人于2022年提出的一种用于时间序列预测的深度学习架构。它通过用指数平滑注意力机制取代标准的自注意力机制，将经典的指数平滑原理直接整合到Transformer框架中。该模型将时间序列分解为水平、增长（趋势）和季节性分量，使其能够利用Transformer的长程依赖建模能力和统计ETS模型的可解释结构。
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来源
- Woo, G., Liu, C., Sahoo, D., Kumar, A., & Hoi, S. (2022). ETSformer: Exponential smoothing transformers for time-series forecasting. arXiv preprint. link ↗
如何引用本页
ScholarGate. (2026, June 2). ETSformer (Exponential Smoothing Transformer). ScholarGate. https://scholargate.app/zh/deep-learning/etsformer
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