Title: VisionTS++: Cross-Modal Time Series Foundation Model with Continual Pre-trained Vision Backbones
Primary area: learning on time series and dynamical systems
Keywords: Time Series Forecasting, Time Series Foundation Model

Abstract: Recent studies have indicated that vision models pre-trained on images can serve as time series foundation models (TSFMs) by reformulating time series forecasting (TSF) as image reconstruction.  However, effective cross-modal transfer from vision to time series remains challenging due to three discrepancies:  (1) the data-modality gap between structured, bounded image data and unbounded, heterogeneous time series;  (2) the multivariate-forecasting gap between fixed RGB-three-channel vision models and time series with arbitrary numbers of variates; and (3) the probabilistic-forecasting gap between the deterministic outputs of vision models and the requirement for uncertainty-aware probabilistic predictions. 
To bridge these gaps, we propose VisonTS++, a TSFM based on continual pre-training of a vision model on large-scale time series.  Our approach introduces three key innovations:  (1) vision-model-based filtering to identify high-quality sequences to stabilize pre-training and mitigate modality gap;  (2) colorized multivariate conversion, encoding multivariate series as multi-subfigure RGB images to enhance cross-variate modeling;  (3) multi-quantile forecasting, using parallel reconstruction heads to generate quantile forecasts without parametric assumptions. 
Experiments show that VisionTS++ achieves state-of-the-art performance in both in-distribution and out-of-distribution forecasting, outperforming specialized TSFMs by 6%–44% in MSE reduction and ranking first in GIFT-Eval benchmark which comprises 23 datasets across 7 domains.  Our work demonstrates that with appropriate adaptation, vision models can effectively generalize to TSF, thus advancing the pursuit of universal TSFMs.

TL;DR: We propose VisionTS++,  which bridges vision and time series via continual pre-training, using data filtering, colorized multivariate conversion, and multi-quantile forecasting to achieve universal time series foundation modeling.