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

• URL: http://arxiv.org/abs/2603.06781v1
• Date: Fri, 06 Mar 2026 18:45:24 GMT
• Title: xaitimesynth: A Python Package for Evaluating Attribution Methods for Time Series with Synthetic Ground Truth
• Abstract summary: We introduce xaitimesynth, a Python package that provides reusable infrastructure for time series evaluation.<n>xaitimesynth generates synthetic time series following an additive model.<n>A fluent data generation API and YAML configuration format allow flexible and reproducible dataset definitions.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Fast, memory-efficient genomic interval tokenizers for modern machine learning [11.571506663357015]
  gtars-tokenizers is a library that maps genomic intervals to a predefined universe or vocabulary of regions.<n> gtars-tokenizers implements two overlap methods (BITS and AIList) and integrates seamlessly with modern ML frameworks.<n>This token-based approach bridges genomics and machine learning, supporting scalable analysis of interval data across diverse environments.
  arXiv  Detail & Related papers  (2025-11-03T13:18:36Z)
• Forging Time Series with Language: A Large Language Model Approach to Synthetic Data Generation [38.95205270397238]
  SDForger is a framework for generating high-quality time series using LLMs.<n>By enabling textual conditioning in the generation process, SDForger paves the way for multimodal modeling.<n>The model is open-sourced at https://github.com/IBM/fms-dgt/tree/main/fms_dgt/public/databuilders/time_series.
  arXiv  Detail & Related papers  (2025-05-21T08:50:49Z)
• tempdisagg: A Python Framework for Temporal Disaggregation of Time Series Data [0.0]
  tempdisagg is a Python framework for temporal disaggregation of time series data.<n>It transforms low-frequency aggregates into consistent, high-frequency estimates.
  arXiv  Detail & Related papers  (2025-03-28T00:15:52Z)
• A Comprehensive Python Library for Deep Learning-Based Event Detection in Multivariate Time Series Data and Information Retrieval in NLP [0.0]
  We present a new deep learning supervised method for detecting events in time series data. It is based on regression instead of binary classification. It does not require labeled datasets where each point is labeled. It only requires reference events defined as time points or intervals of time.
  arXiv  Detail & Related papers  (2023-10-25T09:13:19Z)
• MomentDiff: Generative Video Moment Retrieval from Random to Real [71.40038773943638]
  We provide a generative diffusion-based framework called MomentDiff. MomentDiff simulates a typical human retrieval process from random browsing to gradual localization. We show that MomentDiff consistently outperforms state-of-the-art methods on three public benchmarks.
  arXiv  Detail & Related papers  (2023-07-06T09:12:13Z)
• PyPOTS: A Python Toolkit for Machine Learning on Partially-Observed Time Series [20.491714178518155]
  PyPOTS is an open-source library for data mining and analysis.<n>It provides easy access to diverse algorithms categorized into five tasks.<n>PyPOTS is available on PyPI, Anaconda, and Docker.
  arXiv  Detail & Related papers  (2023-05-30T07:57:05Z)
• TSGM: A Flexible Framework for Generative Modeling of Synthetic Time Series [61.436361263605114]
  Time series data are often scarce or highly sensitive, which precludes the sharing of data between researchers and industrial organizations. We introduce Time Series Generative Modeling (TSGM), an open-source framework for the generative modeling of synthetic time series.
  arXiv  Detail & Related papers  (2023-05-19T10:11:21Z)
• Wild-Time: A Benchmark of in-the-Wild Distribution Shift over Time [69.77704012415845]
  Temporal shifts can considerably degrade performance of machine learning models deployed in the real world. We benchmark 13 prior approaches, including methods in domain generalization, continual learning, self-supervised learning, and ensemble learning. Under both evaluation strategies, we observe an average performance drop of 20% from in-distribution to out-of-distribution data.
  arXiv  Detail & Related papers  (2022-11-25T17:07:53Z)
• Feature-Based Time-Series Analysis in R using the theft Package [0.0]
  Many open-source software packages for computing sets of time-series features exist across multiple programming languages. Here we introduce a solution to these issues in an R software package called theft.
  arXiv  Detail & Related papers  (2022-08-12T07:29:29Z)
• HyperTime: Implicit Neural Representation for Time Series [131.57172578210256]
  Implicit neural representations (INRs) have recently emerged as a powerful tool that provides an accurate and resolution-independent encoding of data. In this paper, we analyze the representation of time series using INRs, comparing different activation functions in terms of reconstruction accuracy and training convergence speed. We propose a hypernetwork architecture that leverages INRs to learn a compressed latent representation of an entire time series dataset.
  arXiv  Detail & Related papers  (2022-08-11T14:05:51Z)
• Scikit-dimension: a Python package for intrinsic dimension estimation [58.8599521537]
  This technical note introduces textttscikit-dimension, an open-source Python package for intrinsic dimension estimation. textttscikit-dimension package provides a uniform implementation of most of the known ID estimators based on scikit-learn application programming interface. We briefly describe the package and demonstrate its use in a large-scale (more than 500 datasets) benchmarking of methods for ID estimation in real-life and synthetic data.
  arXiv  Detail & Related papers  (2021-09-06T16:46:38Z)
• Learning summary features of time series for likelihood free inference [93.08098361687722]
  We present a data-driven strategy for automatically learning summary features from time series data. Our results indicate that learning summary features from data can compete and even outperform LFI methods based on hand-crafted values.
  arXiv  Detail & Related papers  (2020-12-04T19:21:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.