TimeVista: Exploring and Exploiting Vision-Language Models as Judges for Time Series Forecasting
arXiv:2606.16173v1 Announce Type: new Abstract: High-quality time series forecasting is pivotal for real-world decision-making. However, traditional point-wise metrics often fail to reveal complex temporal patterns and align poorly with human intuitive preferences. While the ''LLM-as-a-Judge'' paradigm has revolutionized text evaluation by providing flexible, human-aligned judgment, its application to time series remains largely unexplored. In this paper, we leverage Vision-Language Models (VLMs) as judges for time series forecasting, harnessing their ability to comprehend time series plots gr
The proliferation of advanced Vision-Language Models (VLMs) and the recognized limitations of traditional metrics in time series forecasting are converging to enable new evaluation paradigms.
This development could revolutionize how complex forecasts are evaluated and trusted, leading to more robust decision-making in various critical applications.
The method for assessing time series forecasting model quality moves beyond traditional statistical metrics to incorporate human-aligned, qualitative judgment via VLMs.
- · AI researchers and developers
- · Industries reliant on accurate time series forecasting
- · Platforms integrating VLM-based evaluation tools
- · Developers solely relying on traditional point-wise metrics
- · Systems that cannot integrate VLM outputs
Improved time series forecasting models that are better aligned with practical human preferences.
Increased adoption of VLM-based evaluation across various domains currently using time series forecasts.
New standards for evaluating predictive models emerge, potentially shifting focus from purely quantitative accuracy to 'human-understandable' prediction utility.
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Read at arXiv cs.AI