mlsanityData to 14 Oct 2026

Detecting Change Intervals with Isolation Distributional Kernel

arXiv 2212.14630 ↗First posted 30 Dec 2022cs.LGcited by 4

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Abstract (arXiv)

Detecting abrupt changes in data distribution is one of the most significant tasks in streaming data analysis. Although many unsupervised Change-Point Detection (CPD) methods have been proposed recently to identify those changes, they still suffer from missing subtle changes, poor scalability, or/and sensitivity to outliers. To meet these challenges, we are the first to generalise the CPD problem as a special case of the Change-Interval Detection (CID) problem. Then we propose a CID method, named iCID, based on a recent Isolation Distributional Kernel (IDK). iCID identifies the change interval if there is a high dissimilarity score between two non-homogeneous temporal adjacent intervals. The data-dependent property and finite feature map of IDK enabled iCID to efficiently identify various types of change-points in data streams with the tolerance of outliers. Moreover, the proposed online and offline versions of iCID have the ability to optimise key parameter settings. The effectiveness and efficiency of iCID have been systematically verified on both synthetic and real-world datasets.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 13 of its 55 references that are in the map. First posted on arXiv 30 Dec 2022.

Builds on

13 of 55 references.

References · 13

  1. 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,057
  2. 2017Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networkscited by 879
  3. 2016Kernel Mean Embedding of Distributions: A Review and Beyondcited by 364
  4. 2020Current Time Series Anomaly Detection Benchmarks are Flawed and are Creating the Illusion of Progresscited by 151
  5. 2020An Evaluation of Change Point Detection Algorithmscited by 63
  6. 2020Time Series Change Point Detection with Self-Supervised Contrastive Predictive Codingcited by 58
  7. 2019Kernel Change-point Detection with Auxiliary Deep Generative Modelscited by 33
  8. 2015Scan B-Statistic for Kernel Change-Point Detectioncited by 29
  9. 2013A Nonparametric Approach for Multiple Change Point Analysis of Multivariate Datacited by 28
  10. 2019Nearest-Neighbour-Induced Isolation Similarity and its Impact on Density-Based Clusteringcited by 11
  11. 2020Isolation Distributional Kernel: A New Tool for Point & Group Anomaly Detectioncited by 7
  12. 2020Point-Set Kernel Clusteringcited by 6
1 more
  1. 2013Joint segmentation of multivariate time series with hidden process regression for human activity recognitioncited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 2; 2026: 1.

4 papers.

  1. 2024Anomaly Detection Based on Isolation Mechanisms: A Surveycited by 10
  2. 2025Text Anomaly Detection with Simplified Isolation Kernelcited by 5
  3. 2025Isolation-based Spherical Ensemble Representations for Tabular Anomaly Detectioncited by 0
  4. 2026Stochastic Voronoi Ensembles for Anomaly Detectioncited by 0