Authors: Xiyuan Zhang, Ranak Roy Chowdhury, Rajesh K. Gupta, Jingbo Shang

Published on: February 02, 2024

Impact Score: 8.15

Arxiv code: Arxiv:2402.01801

Summary

• What is new: This research explores new methodologies for applying Large Language Models (LLMs) to time series data analysis, a significant leap beyond their traditional use in text and image processing.
• Why this is important: The challenge lies in adapting LLMs, originally trained on text data, for analyzing numerical time series data in various domains.
• What the research proposes: The paper proposes strategies like direct prompting, time series quantization, alignment techniques, employing vision modality, and combining LLMs with specific tools for effective analysis.
• Results: It provides a comprehensive taxonomy of methodologies, showcases multimodal datasets, and highlights the potential of LLMs in enhancing time series analysis across several domains.

Technical Details

Technological frameworks used: Direct LLM prompting, Quantization, Vision bridging

Models used: Text-to-time series alignment, Multimodal fusion

Data used: Multimodal time series and text datasets

Potential Impact

Climate, IoT, Healthcare, Traffic, Audio, Finance industries

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