Sep 21, 2026 · cs.LGJ/K move · Enter open · S save
Long Ngo, Mohammed Amine Chamli, Jonathan Rivalan, Thomas Jaillon
Traditional evaluation metrics provides numerical values but often lack comprehensibility, hindering effective differentiation of model performances. Our work addresses this challenge by introducing overlay_dx, a novel evaluation metric measuring the performance of time series prediction models. Overlay_dx is a visual metric that represents the percentage of predictions falling within a confidence interval around actual values. Additionally, once evaluation results are plotted, overlay_dx computes the area under the overlay curve, providing a quantitative measure of alignment between predicted and actual values across different thresholds and predictions. Through extensive experiments, we demonstrate that our approach offers a unified evaluation framework that combines both visual and numerical assessments, enabling improved model comparison and providing valuable insights for further research and optimization efforts in time series prediction.