mlsanityData to 14 Oct 2026

Topological Analysis for Detecting Anomalies (TADA) in Time Series

arXiv 2406.06168 ↗First posted 10 Jun 2024math.ST stat.ML stat.THcited by 0

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Abstract (arXiv)

This paper introduces new methodology based on the field of Topological Data Analysis for detecting anomalies in multivariate time series, that aims to detect global changes in the dependency structure between channels. The proposed approach is lean enough to handle large scale datasets, and extensive numerical experiments back the intuition that it is more suitable for detecting global changes of correlation structures than existing methods. Some theoretical guarantees for quantization algorithms based on dependent time sequences are also provided.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 13 of its 50 references that are in the map. First posted on arXiv 10 Jun 2024.

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13 of 50 references.

References · 13

  1. 2015Persistence Images: A Stable Vector Representation of Persistent Homologycited by 236
  2. 2017An introduction to Topological Data Analysis: fundamental and practical aspects for data scientistscited by 172
  3. 2020Exathlon: A Benchmark for Explainable Anomaly Detection over Time Seriescited by 56
  4. 2023Navigating the Metric Maze: A Taxonomy of Evaluation Metrics for Anomaly Detection in Time Seriescited by 24
  5. 2017Minimum Covariance Determinant and Extensionscited by 20
  6. 2023Correlation-aware Spatial-Temporal Graph Learning for Multivariate Time-series Anomaly Detectioncited by 18
  7. 2018The density of expected persistence diagrams and its kernel based estimationcited by 13
  8. 2019Topological Data Analysis for Arrhythmia Detection through Modular Neural Networkscited by 9
  9. 2018Robust Bregman Clusteringcited by 7
  10. 2019Adaptive Anomaly Detection in Chaotic Time Series with a Spatially Aware Echo State Networkcited by 6
  11. 2021Spectral Dependencecited by 6
  12. 2024A provable initialization and robust clustering method for general mixture modelscited by 2
1 more
  1. 2024Time topological analysis of EEG using signature theorycited by 1

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  1. 2025AXIS: Explainable Time Series Anomaly Detection with Large Language Models
  2. 2021Distribution Agnostic Symbolic Representations for Time Series Dimensionality Reduction and Online Anomaly Detection
  3. 2026Learning Unified Representations of Normalcy for Time Series Anomaly Detection
  4. 2026Have an LLM Write Your Anomaly Detector: Autonomous Discovery of Compact, Interpretable Detectors for Time Series
  5. 2023Contaminated Multivariate Time-Series Anomaly Detection with Spatio-Temporal Graph Conditional Diffusion Models
  6. 2026Estimating Mutual Information between Time Series and Temporal Event Sequences Across Diverse Analysis Tasks
  7. 2026Detecting Time Series Anomalies Like an Expert: A Multi-Agent LLM Framework with Specialized Analyzers
  8. 2024Can LLMs Understand Time Series Anomalies?