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arXiv · 2603.06781

xaitimesynth: A Python Package for Evaluating Attribution Methods for Time Series with Synthetic Ground Truth

Gregor Baer
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Abstract

Evaluating time series attribution methods is difficult because real-world datasets rarely provide ground truth for which time points drive a prediction. A common workaround is to generate synthetic data where class-discriminating features are placed at known locations, but each study currently reimplements this from scratch. We introduce xaitimesynth, a Python package that provides reusable infrastructure for this evaluation approach. The package generates synthetic time series following an additive model where each sample is a sum of background signal and a localized, class-discriminating feature, with the feature window automatically tracked as a ground truth mask. A fluent data generation API and YAML configuration format allow flexible and reproducible dataset definitions for both univariate and multivariate time series. The package also provides standard localization metrics, including AUC-PR, AUC-ROC, Relevance Mass Accuracy, and Relevance Rank Accuracy. xaitimesynth is open source and available at https://github.com/gregorbaer/xaitimesynth.

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Gregor Baer. 2026-03-06. xaitimesynth: A Python Package for Evaluating Attribution Methods for Time Series with Synthetic Ground Truth. https://arxiv.org/abs/2603.06781

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