InstructTime++: Time Series Classification with Multimodal Language Modeling via Implicit Feature Enhancement
arXiv:2601.14968v2 Announce Type: replace-cross Abstract: Most existing time series classification methods adopt a discriminative paradigm that maps input sequences directly to one-hot encoded class labels. While effective, this paradigm struggles to incorporate contextual features and fails to capture semantic relationships among classes. To address these limitations, we propose InstructTime, a novel framework that reformulates time series classification as a multimodal generative task. Specifically, continuous numerical sequences, contextual textual features, and task instructions are treate
The proliferation of advanced language models creates new paradigms for integrating diverse data types, making this multimodal approach to time series classification timely.
This research introduces a novel framework that improves time series classification by incorporating contextual features and semantic relationships, moving beyond traditional discriminative methods.
Time series classification can now leverage multimodal language modeling, potentially leading to more accurate and context-aware predictions in various applications.
- · AI researchers
- · Data scientists
- · Industries relying on time series analysis
- · Traditional discriminative models
- · Tools limited to numerical time series data
Improved accuracy and contextual understanding in time series predictions across sectors like finance, healthcare, and manufacturing.
Development of new AI systems that can seamlessly integrate and interpret both numerical and textual data for decision-making.
Enhanced automation in complex systems where nuanced understanding of historical data and context is critical.
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Read at arXiv cs.AI