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Cross-Domain Pre-training with Language Models for Transferable Time Series Representations

Autor: Cheng, Mingyue, Tao, Xiaoyu, Liu, Qi, Zhang, Hao, Chen, Yiheng, Lian, Defu

Advancements in self-supervised pre-training (SSL) have significantly advanced the field of learning transferable time series representations, which can be very useful in enhancing the downstream task. Despite being effective, most existing works str

Externí odkaz: http://arxiv.org/abs/2403.12372

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Advancing Time Series Classification with Multimodal Language Modeling

Autor: Cheng, Mingyue, Chen, Yiheng, Liu, Qi, Liu, Zhiding, Luo, Yucong

For the advancements of time series classification, scrutinizing previous studies, most existing methods adopt a common learning-to-classify paradigm - a time series classifier model tries to learn the relation between sequence inputs and target labe

Externí odkaz: http://arxiv.org/abs/2403.12371

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