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arXiv Machine Learning By Zhenyi Zhu, Jacqueline Pang, Peilin Shen, Tianyi Song, Tingwei Zhang, Keyi Hu, Kangjun Yin, Shiwei Pu, Yingbo Zhou, Chen Shao

SwitchPFN: Shared Switching Dynamics for Frozen In-Context Time Series Classification

Read the original on arXiv Machine Learning →

SwitchPFN introduces a shared projection and regime codebook for time‑series classification with tabular foundation models, preserving local temporal transitions while ensuring consistent feature definitions across samples. The method outperforms existing representations, achieving the highest mean accuracy on evaluated benchmarks and improving the strongest baseline by 4.47% relative. Ablation, sensitivity, and limited‑data experiments confirm the benefits of the proposed representation design.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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