提供可复现的时序归因评估工具，用合成数据精准定位关键时间点。
xaitimesynth: A Python Package for Evaluating Attribution Methods for Time Series with Synthetic Ground Truth
- 基于加法模型生成带已知关键区间的合成时序数据
- 自动标记关键窗口作为真实标签，支持多变量与单变量场景
- 内置多种评估指标，适合研究归因方法的学者和开发者
评估时序归因方法面临挑战，因真实数据通常缺乏预测驱动的时间点标注。现有方法常通过人工构造合成数据，将判别性特征置于已知位置，但每项研究需重复实现。本文提出 xaitimesynth，一个开源 Python 工具包，提供可复用的合成数据生成框架。该工具基于加法模型生成时序数据，每个样本由背景信号与局部类判别特征叠加而成，特征窗口自动记录为真实标签掩码。其灵活的 API 与 YAML 配置格式支持多变量与单变量时序的可复现定义。工具还集成标准定位评估指标，包括 AUC-PR、AUC-ROC、Relevance Mass Accuracy 与 Relevance Rank Accuracy。项目开源，地址为 https://github.com/gregorbaer/xaitimesynth。
原文摘要 · Abstract (English)
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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