From Retrieval to Recognition:Knowledge-Enhanced Post-training of Time Series Foundation Models for Classification

Key points

• Time series foundation models (TSFMs) have achieved notable progress in classification tasks through large-scale pretraining.1
• Rather than pretraining a new foundation model, R2R improves how existing TSFMs are post-trained: it introduces a decoupled query encoder to extract discriminative patterns and retrieve instance-level numerical knowledge from a constructed knowledge base.1
• In this way, R2R allows TSFMs to recall task-relevant patterns learned during pretraining, improving the efficiency of knowledge utilization in downstream classification transfer.1
• Experiments on 158 time series datasets show that R2R consistently improves the classification performance of state-of-the-art TSFMs. Ablation and knowledge-enhanced studies further validate the effectiveness of the proposed framework.1

Sources (1)

• [1]From Retrieval to Recognition:Knowledge-Enhanced Post-training of Time Series Foundation Models for Classification
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 19, 10:09 AM

  To this end, we propose Retrieval-to-Recognition (R2R), a plug-and-play post-training paradigm that adapts pretrained TSFMs to time series classification.

  Time series foundation models (TSFMs) have achieved notable progress in classification tasks through large-scale pretraining.

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