Sequential change-point detection in time series models based on pairwise likelihood

2017 ◽  
Author(s):  
Chun-Yip Yau ◽  
Sze Him Leung ◽  
Wai Leong Ng
Keyword(s):  
Time Series ◽  
Change Point ◽  
Change Point Detection ◽  
Time Series Models ◽  
Pairwise Likelihood ◽  
Sequential Change ◽  
Point Detection

Sequential Change-Point Detection Methods for Nonstationary Time Series

Technometrics ◽  
2008 ◽  
Vol 50 (1) ◽  
pp. 40-52 ◽  
Author(s):  
Hyunyoung Choi ◽  
Hernando Ombao ◽  
Bonnie Ray
Keyword(s):  
Time Series ◽  
Change Point ◽  
Nonstationary Time Series ◽  
Change Point Detection ◽  
Detection Methods ◽  
Sequential Change ◽  
Point Detection

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.

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