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Algorithmic Recourse in Abnormal Multivariate Time Series

arXiv 2309.16896 ↗First posted 28 Sep 2023cs.LGcited by 0

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Abstract (arXiv)

Algorithmic recourse provides actionable recommendations to alter unfavorable predictions of machine learning models, enhancing transparency through counterfactual explanations. While significant progress has been made in algorithmic recourse for static data, such as tabular and image data, limited research explores recourse for multivariate time series, particularly for reversing abnormal time series. This paper introduces Recourse in time series Anomaly Detection (RecAD), a framework for addressing anomalies in multivariate time series using backtracking counterfactual reasoning. By modeling the causes of anomalies as external interventions on exogenous variables, RecAD predicts recourse actions to restore normal status as counterfactual explanations, where the recourse function, responsible for generating actions based on observed data, is trained using an end-to-end approach. Experiments on synthetic and real-world datasets demonstrate its effectiveness.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 14 of its 28 references that are in the map. First posted on arXiv 28 Sep 2023.

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References · 14

  1. 2020Algorithmic Recourse: from Counterfactual Explanations to Interventionscited by 250
  2. 2022TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Datacited by 214
  3. 2020A review on outlier/anomaly detection in time series datacited by 189
  4. 2020Algorithmic recourse under imperfect causal knowledge: a probabilistic approachcited by 140
  5. 2020On the Fairness of Causal Algorithmic Recoursecited by 70
  6. 2020Exathlon: A Benchmark for Explainable Anomaly Detection over Time Seriescited by 56
  7. 2021On the Adversarial Robustness of Causal Algorithmic Recoursecited by 56
  8. 2020SCOUT: Self-aware Discriminant Counterfactual Explanationscited by 52
  9. 2021Interpretable Models for Granger Causality Using Self-explaining Neural Networkscited by 45
  10. 2022Robust and Explainable Autoencoders for Unsupervised Time Series Outlier Detection---Extended Versioncited by 19
  11. 2020Linear Classifiers that Encourage Constructive Adaptationcited by 16
  12. 2022Backtracking Counterfactualscited by 16
2 more
  1. 2020Counterfactual Explanation Based on Gradual Construction for Deep Networkscited by 12
  2. 2022Framing Algorithmic Recourse for Anomaly Detectioncited by 5

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