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Large Vision Models show promise for time series classification, but not forecasting

By PulseAugur Editorial · [1 sources] ·

A research paper explored the utility of Large Vision Models (LVMs) for time series analysis, comparing them against established imaging methods and baselines across various tasks. The study found that LVMs are effective for time series classification but struggle with forecasting, with current best-performing LVM forecasters showing limitations in specific model types, imaging techniques, and the ability to leverage long look-back windows. The authors hope this foundational research will guide future work on LVMs and multimodal solutions for time series problems. AI

IMPACT LVMs show potential for time series classification, but their forecasting capabilities require further development and specialized approaches.

RANK_REASON Research paper published on arXiv exploring the application of LVMs to time series analysis. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Large Vision Models show promise for time series classification, but not forecasting

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Research paper published on arXiv exploring the application of LVMs to time series analysis. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Ziming Zhao, ChengAo Shen, Hanghang Tong, Dongjin Song, Zhigang Deng, Qingsong Wen, Jingchao Ni ·

    From Images to Signals: Are Large Vision Models Useful for Time Series Analysis?

    arXiv:2505.24030v3 Announce Type: replace-cross Abstract: Transformer-based models have gained increasing attention in time series research, driving interest in Large Language Models (LLMs) and foundation models for time series analysis. As the field moves toward multi-modality, …

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