Kavli Affiliate: Zhuo Li
| First 5 Authors: Lefei Shen, Lefei Shen, , ,
| Summary:
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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