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Found 2 papers, 1 papers with code
TimeDRL: Disentangled Representation Learning for Multivariate Time-Series
Multivariate time-series data in numerous real-world applications (e. g., healthcare and industry) are informative but challenging due to the lack of labels and high dimensionality.
LLM4TS: Aligning Pre-Trained LLMs as Data-Efficient Time-Series Forecasters
Recently, researchers have leveraged the representation learning transferability of pre-trained Large Language Models (LLMs) to handle limited non-linguistic datasets effectively.
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