Optimizing Visualization for Time Series Analysis via Vision-embedded Bayesian Optimization

Time-series analysis (TSA) is fundamental across many real-world domains, supporting forecasting, anomaly detection, and decision making. Recent vision-based approaches convert time-series data into images and apply models for TSA. However, these methods typically use fixed or manually selected visualization settings, even though different visual presentations may lead to different model performance. Therefore, we introduce Visual Presentation Optimization (VPO), a new formulation that casts time-series visualization design as an optimization problem to improve vision-based TSA performance for a given task and model. To efficiently explore the large combinatorial configuration space, we propose Vision-embedded Bayesian Optimization (VeBO), which incorporates visual embeddings into a deep kernel learning-based Bayesian Optimization surrogate to predict performance from visualization settings and guide the search. Extensive experiments show that optimizing visual presentations without additional training improves performance efficiently across multiple TSA tasks and models, highlighting VPO as an effective, model-agnostic approach for vision-based time-series understanding. Code will be published upon acceptance.