Change Point Detection in Piecewise Stationary Time Series for Farm Animal Behavior Analysis

2017 ◽  
pp. 369-375
Author(s):  
Sandra Breitenberger ◽  
Dmitry Efrosinin ◽  
Wolfgang Auer ◽  
Andreas Deininger ◽  
Ralf Waßmuth
Keyword(s):  
Time Series ◽  
Behavior Analysis ◽  
Animal Behavior ◽  
Change Point ◽  
Change Point Detection ◽  
Farm Animal ◽  
Stationary Time Series ◽  
Stationary Time ◽  
Point Detection

scholarly journals METHOD FOR CHANGE-POINT DETECTION IN PIECEWISE STATIONARY TIME-SERIES

2017 ◽  
Vol 0 (16) ◽  
pp. 120-127
Author(s):  
Олексій Володимирович Дергунов ◽  
Ганна Вадимівна Мартинюк
Keyword(s):  
Time Series ◽  
Change Point ◽  
Change Point Detection ◽  
Stationary Time Series ◽  
Stationary Time ◽  
Point Detection

BOOTSTRAP INFERENCE FOR MULTIPLE CHANGE-POINTS IN TIME SERIES

2021 ◽  
pp. 1-41
Author(s):  
Wai Leong Ng ◽  
Shenyi Pan ◽  
Chun Yip Yau
Keyword(s):  
Time Series ◽  
Change Point ◽  
Financial Time Series ◽  
Change Point Detection ◽  
Stationary Time Series ◽  
Change Points ◽  
Finite Sample ◽  
Sample Distribution ◽  
Stationary Time ◽  
Point Estimators

In this paper, we propose two bootstrap procedures, namely parametric and block bootstrap, to approximate the finite sample distribution of change-point estimators for piecewise stationary time series. The bootstrap procedures are then used to develop a generalized likelihood ratio scan method (GLRSM) for multiple change-point inference in piecewise stationary time series, which estimates the number and locations of change-points and provides a confidence interval for each change-point. The computational complexity of using GLRSM for multiple change-point detection is as low as $O(n(\log n)^{3})$ for a series of length n. Extensive simulation studies are provided to demonstrate the effectiveness of the proposed methodology under different scenarios. Applications to financial time series are also 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.

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