mlsanityData to 7 Oct 2026

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

arXiv 2505.24030 ↗First posted 29 May 2025cs.LG cs.AI cs.CVcited by 7

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Abstract (arXiv)

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, Large Vision Models (LVMs) are emerging as a promising direction. In the past, the effectiveness of Transformer and LLMs in time series has been debated. When it comes to LVMs, a similar question arises: are LVMs truely useful for time series analysis? To address it, we design and conduct the first principled study involving 4 LVMs, 8 imaging methods, 18 datasets and 26 baselines across both high-level (classification) and low-level (forecasting) tasks, with extensive ablation analysis. Our findings indicate LVMs are indeed useful for time series classification but face challenges in forecasting. Although effective, the contemporary best LVM forecasters are limited to specific types of LVMs and imaging methods, exhibit a bias toward forecasting periods, and have limited ability to utilize long look-back windows. We hope our findings could serve as a cornerstone for future research on LVM- and multimodal-based solutions to different time series tasks.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 62 of its 67 references that are in the map. First posted on arXiv 29 May 2025.

Builds on

62 of 67 references.

References · 62

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38 more
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50 most cited of 62 All 62 (JSON)

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 3; 2026: 4.

7 papers.

  1. 2025SVTime: Small Time Series Forecasting Models Informed by "Physics" of Large Vision Model Forecasterscited by 2
  2. 2025Rating Quality of Diverse Time Series Data by Meta-learning from LLM Judgmentcited by 1
  3. 2025CLIPTime: Time-Aware Multimodal Representation Learning from Images and Textcited by 0
  4. 2026From Consistency to Complementarity: Aligned and Disentangled Multi-modal Learning for Time Series Understanding and Reasoningcited by 0
  5. 2026VAN-AD: Visual Masked Autoencoder with Normalizing Flow For Time Series Anomaly Detectioncited by 0
  6. 2026Empowering VLMs for Few-Shot Multimodal Time Series Classification via Tailored Agentic Reasoningcited by 0
  7. 2026ViSTA: A Simple Bridge Extends Visual Alignment to Clinical Time-Series Understanding in Multimodal LLMscited by 0