VisionTS++: Cross-Modal Time Series Foundation Model with Continual Pre-trained Visual Backbones

• URL: http://arxiv.org/abs/2508.04379v1
• Date: Wed, 06 Aug 2025 12:17:09 GMT
• Title: VisionTS++: Cross-Modal Time Series Foundation Model with Continual Pre-trained Visual Backbones
• Authors: Lefei Shen, Mouxiang Chen, Xu Liu, Han Fu, Xiaoxue Ren, Jianling Sun, Zhuo Li, Chenghao Liu,
• Abstract summary: We propose VisionTS++, a vision-model-based TSFM that performs continual pre-training on large-scale time series datasets.<n>Our work establishes a new paradigm for cross-modal knowledge transfer, advancing the development of universal TSFMs.
• Score: 27.97547118858576
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent studies have revealed that vision models pre-trained on images can perform well in time series forecasting by reformulating forecasting as an image reconstruction task, suggesting their potential as universal time series foundation models. However, effective cross-modal transfer from vision to time series remains challenging due to three key discrepancies: (1) data-modality gap between structured, bounded image data and unbounded, heterogeneous time series; (2) multivariate-forecasting gap between standard RGB three-channel-based vision models and the need to model time series with arbitrary numbers of variates; and (3) probabilistic-forecasting gap between the deterministic output formats of most vision models and the requirement for uncertainty-aware probabilistic predictions. To bridge these gaps, we propose VisionTS++, a vision-model-based TSFM that performs continual pre-training on large-scale time series datasets, including 3 innovations: (1) a vision-model-based filtering mechanism to identify high-quality time series data, thereby mitigating modality gap and improving pre-training stability, (2) a colorized multivariate conversion method that transforms multivariate time series into multi-subfigure RGB images, capturing complex inter-variate dependencies; and (3) a multi-quantile forecasting approach using parallel reconstruction heads to generate forecasts of different quantile levels, thus more flexibly approximating arbitrary output distributions without restrictive prior distributional assumptions. Evaluated on both in-distribution and out-of-distribution TSF benchmarks, \model achieves SOTA results, outperforming specialized TSFMs by 6%-44% in MSE reduction and ranking first in 9 out of 12 probabilistic forecasting settings. Our work establishes a new paradigm for cross-modal knowledge transfer, advancing the development of universal TSFMs.

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