Causal Discovery-Driven Change Point Detection in Time Series

• URL: http://arxiv.org/abs/2407.07290v1
• Date: Wed, 10 Jul 2024 00:54:42 GMT
• Title: Causal Discovery-Driven Change Point Detection in Time Series
• Authors: Shanyun Gao, Raghavendra Addanki, Tong Yu, Ryan A. Rossi, Murat Kocaoglu,
• Abstract summary: Change point detection in time series seeks to identify times when the probability distribution of time series changes. In practical applications, we may be interested only in certain components of the time series, exploring abrupt changes in their distributions.
• Score: 32.424281626708336
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Change point detection in time series seeks to identify times when the probability distribution of time series changes. It is widely applied in many areas, such as human-activity sensing and medical science. In the context of multivariate time series, this typically involves examining the joint distribution of high-dimensional data: If any one variable changes, the whole time series is assumed to have changed. However, in practical applications, we may be interested only in certain components of the time series, exploring abrupt changes in their distributions in the presence of other time series. Here, assuming an underlying structural causal model that governs the time-series data generation, we address this problem by proposing a two-stage non-parametric algorithm that first learns parts of the causal structure through constraint-based discovery methods. The algorithm then uses conditional relative Pearson divergence estimation to identify the change points. The conditional relative Pearson divergence quantifies the distribution disparity between consecutive segments in the time series, while the causal discovery method enables a focus on the causal mechanism, facilitating access to independent and identically distributed (IID) samples. Theoretically, the typical assumption of samples being IID in conventional change point detection methods can be relaxed based on the Causal Markov Condition. Through experiments on both synthetic and real-world datasets, we validate the correctness and utility of our approach.

Related papers

• Causal Discovery in Semi-Stationary Time Series [32.424281626708336]
  We propose a constraint-based, non-parametric algorithm for discovering causal relations in observational time series. We show that this algorithm is sound in identifying causal relations on discrete time series.
  arXiv  Detail & Related papers  (2024-07-10T00:55:38Z)
• Understanding Time Series Anomaly State Detection through One-Class Classification [13.822504564241454]
  In this article, we try to re-understand and define the time series anomaly detection problem through one-class classification (OCC) We first use processes and hypothesis testing to strictly define the 'time series anomaly state detection problem', and its corresponding anomalies. Then, we use the time series classification dataset to construct an artificial dataset corresponding to the problem. We compile 38 anomaly detection algorithms and correct some of the algorithms to adapt to handle this problem.
  arXiv  Detail & Related papers  (2024-02-03T03:43:04Z)
• Graph Spatiotemporal Process for Multivariate Time Series Anomaly Detection with Missing Values [67.76168547245237]
  We introduce a novel framework called GST-Pro, which utilizes a graphtemporal process and anomaly scorer to detect anomalies. Our experimental results show that the GST-Pro method can effectively detect anomalies in time series data and outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-01-11T10:10:16Z)
• Compatible Transformer for Irregularly Sampled Multivariate Time Series [75.79309862085303]
  We propose a transformer-based encoder to achieve comprehensive temporal-interaction feature learning for each individual sample. We conduct extensive experiments on 3 real-world datasets and validate that the proposed CoFormer significantly and consistently outperforms existing methods.
  arXiv  Detail & Related papers  (2023-10-17T06:29:09Z)
• HFN: Heterogeneous Feature Network for Multivariate Time Series Anomaly Detection [2.253268952202213]
  We propose a novel semi-supervised anomaly detection framework based on a heterogeneous feature network (HFN) for MTS. We first combine the embedding similarity subgraph generated by sensor embedding and feature value similarity subgraph generated by sensor values to construct a time-series heterogeneous graph. This approach fuses the state-of-the-art technologies of heterogeneous graph structure learning (HGSL) and representation learning.
  arXiv  Detail & Related papers  (2022-11-01T05:01:34Z)
• Causality-Based Multivariate Time Series Anomaly Detection [63.799474860969156]
  We formulate the anomaly detection problem from a causal perspective and view anomalies as instances that do not follow the regular causal mechanism to generate the multivariate data. We then propose a causality-based anomaly detection approach, which first learns the causal structure from data and then infers whether an instance is an anomaly relative to the local causal mechanism. We evaluate our approach with both simulated and public datasets as well as a case study on real-world AIOps applications.
  arXiv  Detail & Related papers  (2022-06-30T06:00:13Z)
• Deep learning model solves change point detection for multiple change types [69.77452691994712]
  A change points detection aims to catch an abrupt disorder in data distribution. We propose an approach that works in the multiple-distributions scenario.
  arXiv  Detail & Related papers  (2022-04-15T09:44:21Z)
• E-detectors: a nonparametric framework for sequential change detection [86.15115654324488]
  We develop a fundamentally new and general framework for sequential change detection. Our procedures come with clean, nonasymptotic bounds on the average run length. We show how to design their mixtures in order to achieve both statistical and computational efficiency.
  arXiv  Detail & Related papers  (2022-03-07T17:25:02Z)
• TACTiS: Transformer-Attentional Copulas for Time Series [76.71406465526454]
  estimation of time-varying quantities is a fundamental component of decision making in fields such as healthcare and finance. We propose a versatile method that estimates joint distributions using an attention-based decoder. We show that our model produces state-of-the-art predictions on several real-world datasets.
  arXiv  Detail & Related papers  (2022-02-07T21:37:29Z)
• Anomaly Attribution of Multivariate Time Series using Counterfactual Reasoning [7.616400192843963]
  We develop a novel attribution scheme for multivariate time series relying on counterfactual reasoning. We detect anomalous intervals using the Maximally Divergent Interval (MDI) algorithm.
  arXiv  Detail & Related papers  (2021-09-14T10:15:52Z)

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.