Transformers in Time Series: A Survey

🤖 Plain-English Summary

Transformers have achieved superior performances in many tasks in natural language processing and computer vision, which also triggered great interest in the time series community. Empirically, we perform robust analysis, model size analysis, and seasonal-trend decomposition analysis to study how Transformers perform in time series.

🔑 Key Findings

• Among multiple advantages of Transformers, the ability to capture long-range dependencies and interactions is especially attractive for time series modeling, leading to exciting progress in various time series applications.
• In this paper, we systematically review Transformer schemes for time series modeling by highlighting their strengths as well as limitations.
• In particular, we examine the development of time series Transformers in two perspectives.

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