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New research questions superposition in Transformers for time series forecasting

作者 PulseAugur 编辑部 · [4 个来源] ·

Researchers have investigated the internal representations of transformer models used for time series forecasting, finding that complex mechanisms like superposition are not necessary for competitive performance. Studies using sparse autoencoders on models like PatchTST revealed that representations remain sparse and stable, even with expanded dictionaries and minimal sensitivity to latent interventions. Concurrently, a survey and a new method called DyWPE highlight the importance of positional encoding in transformer-based time series analysis, with DyWPE demonstrating improved accuracy by being signal-aware. AI

影响 Suggests that simpler mechanisms may suffice for transformers in time series tasks, potentially simplifying model design and improving efficiency.

排序理由 Multiple arXiv papers discussing transformer models for time series forecasting, including mechanistic interpretability and positional encoding.

在 arXiv cs.LG 阅读 →

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New research questions superposition in Transformers for time series forecasting

报道来源 [4]

  1. arXiv cs.LG TIER_1 English(EN) · Alper Y{\i}ld{\i}r{\i}m ·

    Superposition Is Not Necessary: A Mechanistic Interpretability Analysis of Transformer Representations for Time Series Forecasting

    arXiv:2605.05151v1 Announce Type: new Abstract: Transformer architectures have been widely adopted for time series forecasting, yet whether the representational mechanisms that make them powerful in NLP actually engage on time series data remains unexplored. The persistent compet…

  2. arXiv cs.LG TIER_1 English(EN) · Habib Irani, Vangelis Metsis ·

    Positional Encoding in Transformer-Based Time Series Models: A Survey

    arXiv:2502.12370v3 Announce Type: replace Abstract: Recent advancements in transformer-based models have greatly improved time series analysis, providing robust solutions for tasks such as forecasting, anomaly detection, and classification. A crucial element of these models is po…

  3. arXiv cs.LG TIER_1 English(EN) · Habib Irani, Vangelis Metsis ·

    DyWPE: Signal-Aware Dynamic Wavelet Positional Encoding for Time Series Transformers

    arXiv:2509.14640v2 Announce Type: replace Abstract: Existing positional encoding methods in transformers are fundamentally signal-agnostic, deriving positional information solely from sequence indices while ignoring the underlying signal characteristics. This limitation is partic…

  4. arXiv cs.AI TIER_1 English(EN) · Alper Yıldırım ·

    Superposition Is Not Necessary: A Mechanistic Interpretability Analysis of Transformer Representations for Time Series Forecasting

    Transformer architectures have been widely adopted for time series forecasting, yet whether the representational mechanisms that make them powerful in NLP actually engage on time series data remains unexplored. The persistent competitiveness of simple linear models such as DLinea…

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