是的，Transformer 在時間序列預測上依然強大（結合 Autoformer）
Original: Yes, Transformers are Effective for Time Series Forecasting (+ Autoformer)
In recent years, the academic community has engaged in heated debate over whether Transformers are suitable for time series forecasting —…
針對學術界對 Transformer 是否適用於時間序列預測的質疑，Hugging Face 撰文平反。文章重點介紹已整合至其函式庫的 Autoformer 模型，該模型透過「序列分解」與「自相關機制」克服傳統 Transformer 的效能瓶頸。這證明了只要設計得當，Transformer 在長期時間序列預測（LSTF）上依然能展現卓越的準確度。
In recent years, the academic community has engaged in heated debate over whether Transformers are suitable for time series forecasting — particularly after some research showed that simple linear models (such as DLinear) outperformed complex Transformers on certain benchmarks. In response to this controversy, Hugging Face published this blog post to deeply explore and demonstrate that, with the right architectural adjustments, Transformers remain a powerful tool for time series forecasting.
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