mlsanityData to 14 Oct 2026

Unifying Explainable Anomaly Detection and Root Cause Analysis in Dynamical Systems

arXiv 2502.12086 ↗First posted 17 Feb 2025cs.LG stat.MLcited by 4

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Abstract (arXiv)

Dynamical systems, prevalent in various scientific and engineering domains, are susceptible to anomalies that can significantly impact their performance and reliability. This paper addresses the critical challenges of anomaly detection, root cause localization, and anomaly type classification in dynamical systems governed by ordinary differential equations (ODEs). We define two categories of anomalies: cyber anomalies, which propagate through interconnected variables, and measurement anomalies, which remain localized to individual variables. To address these challenges, we propose the Interpretable Causality Ordinary Differential Equation (ICODE) Networks, a model-intrinsic explainable learning framework. ICODE leverages Neural ODEs for anomaly detection while employing causality inference through an explanation channel to perform root cause analysis (RCA), elucidating why specific time periods are flagged as anomalous. ICODE is designed to simultaneously perform anomaly detection, RCA, and anomaly type classification within a single, interpretable framework. Our approach is grounded in the hypothesis that anomalies alter the underlying ODEs of the system, manifesting as changes in causal relationships between variables. We provide a theoretical analysis of how perturbations in learned model parameters can be utilized to identify anomalies and their root causes in time series data. Comprehensive experimental evaluations demonstrate the efficacy of ICODE across various dynamical systems, showcasing its ability to accurately detect anomalies, classify their types, and pinpoint their origins.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 10 of its 28 references that are in the map. First posted on arXiv 17 Feb 2025.

Builds on

10 of 28 references.

References · 10

  1. 2018Neural Ordinary Differential Equationscited by 3,779
  2. 2021Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancycited by 394
  3. 2020A review on outlier/anomaly detection in time series datacited by 189
  4. 2019Neural Jump Stochastic Differential Equationscited by 150
  5. 2019Causal structure based root cause analysis of outlierscited by 65
  6. 2021Anomaly Detection and Failure Root Cause Analysis in (Micro)Service-Based Cloud Applications: A Surveycited by 48
  7. 2021Interpretable Models for Granger Causality Using Self-explaining Neural Networkscited by 45
  8. 2020Deep Learning-Based Anomaly Detection in Cyber-Physical Systems: Progress and Opportunitiescited by 27
  9. 2020A Survey of Machine Learning Methods for Detecting False Data Injection Attacks in Power Systemscited by 8
  10. 2022On Root Cause Localization and Anomaly Mitigation through Causal Inferencecited by 6

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 3.

4 papers.

  1. 2025RFOD: Random Forest-based Outlier Detection for Tabular Datacited by 0
  2. 2026Root Cause Analysis of Measurement and Mechanistic Anomaliescited by 0
  3. 2026Power Interpretable Causal ODE Networks: A Unified Model for Explainable Anomaly Detection and Root Cause Analysis in Power Systemscited by 0
  4. 2026Causally-Constrained Probabilistic Forecasting for Time-Series Anomaly Detectioncited by 0