Mohammed Amine Chamli

Thomas Jaillon

Long H. Ngo

Jonathan Rivalan

Traditional evaluation metrics provides numerical values but often lack comprehensibility, hindering effective differentiation of model performances. Our work addresses this challenge by introduc-
ing 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 inter-
val 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, en-
abling improved model comparison and providing valuable insights for further research and optimization efforts in time series prediction.

Conference
2025 - International Conference on Optimization and Learning (OLA2025)