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September 24, 20250 citationsOpen Access

Cross-Model and Cross-Modality Learning with PLMs for Time Series Forecasting

CC-Time: Cross-Model and Cross-Modality Time Series Forecasting

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Authors

PCPeng ChenYWYihang WangYSYang Shu

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Overview

CC-Time achieves state-of-the-art prediction accuracy in time series modeling, indicating improved integration of language models and time series attributes.

Key Points

  • CC-Time achieves state-of-the-art predictive accuracy across various real-world datasets, showcasing its effectiveness.
  • The approach combines temporal dependency modeling and channel correlations from both time series and corresponding text descriptions.
  • Using cross-model fusion, CC-Time integrates knowledge from pre-trained language models and traditional time series models.
  • Extensive experiments demonstrate improvements in prediction accuracy in both full-data training and few-shot learning scenarios.

Cite This Study

Chen et al. (2025) studied this question.

synapsesocial.com/papers/68d6e1978b2b6861e4c4049fhttps://doi.org/10.48550/arxiv.2508.12235
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