scholarly journals Change-point detection, segmentation, and related topics

2020 ◽  
Vol 68 ◽  
pp. 97-122
Author(s):  
Jean-Marc Bardet ◽  
Vincent Brault ◽  
Serguei Dachian ◽  
Farida Enikeeva ◽  
Bruno Saussereau
Keyword(s):  
Time Series ◽  
Change Point ◽  
Point Estimation ◽  
Change Point Detection ◽  
Autoregressive Models ◽  
Change Point Estimation ◽  
Adaptive Penalty ◽  
Singularity Point ◽  
Dependency Structures ◽  
Point Detection

Recent contributions to change-point detection, segmentation and inference for non-regular models are presented. Various problems are considered including the multiple change-point estimation with adaptive penalty for time series with different dependency structures, estimation of the singularity point in cusp-type models, inference for thresholded autoregressive models, and cross-segmentation of matrices.

scholarly journals Consistent and powerful graph-based change-point test for high-dimensional data

2017 ◽  
Vol 114 (15) ◽  
pp. 3873-3878 ◽  
Author(s):  
Xiaoping Shi ◽  
Yuehua Wu ◽  
Calyampudi Radhakrishna Rao
Keyword(s):  
Change Point ◽  
Hamiltonian Path ◽  
Point Estimation ◽  
Change Point Detection ◽  
High Dimensional ◽  
Simulation Studies ◽  
Change Point Estimation ◽  
Change Point Test ◽  
Point Test ◽  
Point Detection

A change-point detection is proposed by using a Bayesian-type statistic based on the shortest Hamiltonian path, and the change-point is estimated by using ratio cut. A permutation procedure is applied to approximate the significance of Bayesian-type statistics. The change-point test is proven to be consistent, and an error probability in change-point estimation is provided. The test is very powerful against alternatives with a shift in variance and is accurate in change-point estimation, as shown in simulation studies. Its applicability in tracking cell division is illustrated.

scholarly journals Change Point Enhanced Anomaly Detection for IoT Time Series Data

Water ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 1633
Author(s):  
Elena-Simona Apostol ◽  
Ciprian-Octavian Truică ◽  
Florin Pop ◽  
Christian Esposito
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Change Point ◽  
Time Series Data ◽  
Multivariate Time Series ◽  
Change Point Detection ◽  
Change Points ◽  
Series Data ◽  
Prediction And Forecasting ◽  
Point Detection

Due to the exponential growth of the Internet of Things networks and the massive amount of time series data collected from these networks, it is essential to apply efficient methods for Big Data analysis in order to extract meaningful information and statistics. Anomaly detection is an important part of time series analysis, improving the quality of further analysis, such as prediction and forecasting. Thus, detecting sudden change points with normal behavior and using them to discriminate between abnormal behavior, i.e., outliers, is a crucial step used to minimize the false positive rate and to build accurate machine learning models for prediction and forecasting. In this paper, we propose a rule-based decision system that enhances anomaly detection in multivariate time series using change point detection. Our architecture uses a pipeline that automatically manages to detect real anomalies and remove the false positives introduced by change points. We employ both traditional and deep learning unsupervised algorithms, in total, five anomaly detection and five change point detection algorithms. Additionally, we propose a new confidence metric based on the support for a time series point to be an anomaly and the support for the same point to be a change point. In our experiments, we use a large real-world dataset containing multivariate time series about water consumption collected from smart meters. As an evaluation metric, we use Mean Absolute Error (MAE). The low MAE values show that the algorithms accurately determine anomalies and change points. The experimental results strengthen our assumption that anomaly detection can be improved by determining and removing change points as well as validates the correctness of our proposed rules in real-world scenarios. Furthermore, the proposed rule-based decision support systems enable users to make informed decisions regarding the status of the water distribution network and perform effectively predictive and proactive maintenance.

scholarly journals WATCH: Wasserstein Change Point Detection for High-Dimensional Time Series Data

2021 ◽  
Author(s):  
Kamil Faber ◽  
Roberto Corizzo ◽  
Bartlomiej Sniezynski ◽  
Michael Baron ◽  
Nathalie Japkowicz
Keyword(s):  
Time Series ◽  
Change Point ◽  
Time Series Data ◽  
Change Point Detection ◽  
Series Data ◽  
High Dimensional ◽  
Point Detection
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