ViTime: Foundation Model for Time Series Forecasting Powered by Vision Intelligence

TL;DR

ViTime introduces a novel vision intelligence-powered framework for time series forecasting, shifting from traditional numerical fitting to image-based operations, achieving state-of-the-art results and enhanced robustness.

Contribution

The paper pioneers a vision-based foundation model for TSF, incorporating a binary image-based metric space and a synthesis algorithm to improve generalizability and performance.

Findings

Outperforms TimesFM by 9-15% in zero-shot scenarios

Surpasses leading models with only 10% fine-tuning data

Demonstrates robustness against data missingness and perturbations

Abstract

Time series forecasting (TSF) possesses great practical values in various fields, including power and energy, transportation, etc. TSF methods have been studied based on knowledge from classical statistics to modern deep learning. Yet, all of them were developed based on one fundamental concept, the numerical data fitting. Thus, the models developed have long been known to be problem-specific and lacking application generalizability. Practitioners expect a TSF foundation model that serves TSF tasks in different applications. The central question is then how to develop such a TSF foundation model. This paper offers one pioneering study in the TSF foundation model development method and proposes a vision intelligence-powered framework, ViTime, for the first time. ViTime fundamentally shifts TSF from numerical fitting to operations based on a binary image-based time series metric space and naturally supports both point and probabilistic forecasting. We also provide rigorous theoretical analyses of ViTime, including quantization-induced system error bounds and principled strategies for optimal parameter selection. Furthermore, we propose RealTS, an innovative synthesis algorithm generating diverse and realistic training samples, effectively enriching the training data and significantly enhancing model generalizability. Extensive experiments demonstrate ViTime's state-of-the-art performance. In zero-shot scenarios, ViTime outperforms TimesFM by 9-15\%. With just 10\% fine-tuning data, ViTime surpasses both leading foundation models and fully-supervised benchmarks, a gap that widens with 100\% fine-tuning. ViTime also exhibits exceptional robustness, effectively handling missing data and outperforming TimesFM by 20-30\% under various data perturbations, validating the power of its visual space data operation paradigm.