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Found 4 papers, 1 papers with code
Advancing Time Series Classification with Multimodal Language Modeling
In this work, we propose InstructTime, a novel attempt to reshape time series classification as a learning-to-generate paradigm.
Generative Pretrained Hierarchical Transformer for Time Series Forecasting
Secondly, the one-step generation schema is widely followed, which necessitates a customized forecasting head and overlooks the temporal dependencies in the output series, and also leads to increased training costs under different horizon length settings.
Unlocking the Potential of Large Language Models for Explainable Recommendations
In this study, we propose LLMXRec, a simple yet effective two-stage explainable recommendation framework aimed at further boosting the explanation quality by employing LLMs.
FormerTime: Hierarchical Multi-Scale Representations for Multivariate Time Series Classification
Deep learning-based algorithms, e. g., convolutional networks, have significantly facilitated multivariate time series classification (MTSC) task.
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