scholarly journals Real-Time Change Point Detection with Application to Smart Home Time Series Data

2019 ◽  
Vol 31 (5) ◽  
pp. 1010-1023 ◽  
Author(s):  
Samaneh Aminikhanghahi ◽  
Tinghui Wang ◽  
Diane J. Cook
Keyword(s):  
Time Series ◽  
Real Time ◽  
Change Point ◽  
Smart Home ◽  
Time Series Data ◽  
Time Change ◽  
Change Point Detection ◽  
Series Data ◽  
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.

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

scholarly journals Change-point detection in time-series data by relative density-ratio estimation

2013 ◽  
Vol 43 ◽  
pp. 72-83 ◽  
Author(s):  
Song Liu ◽  
Makoto Yamada ◽  
Nigel Collier ◽  
Masashi Sugiyama
Keyword(s):  
Time Series ◽  
Relative Density ◽  
Change Point ◽  
Time Series Data ◽  
Density Ratio ◽  
Change Point Detection ◽  
Series Data ◽  
Ratio Estimation ◽  
Point Detection

scholarly journals Human Activity Prediction in Smart Home Time Series Data by using Change Point Detection

2020 ◽  
Vol 9 (5) ◽  
pp. 1268-1271
Keyword(s):  
Time Series ◽  
Change Point ◽  
Time Series Data ◽  
Proper Time ◽  
Arima Model ◽  
Computational Cost ◽  
Change Point Detection ◽  
Series Data ◽  
Detection Accuracy ◽  
Point Detection

A Sensor is attached to a house which send message now and then about the housemates or the old people activities. Whether they had taken food in the correct time or not and other activities they perform in the proper time or not. Change Point Detection (CPD) is a matter of perspective which discovers deviating from what are normal or usual changes within the housemates. Any abnormal changes in the housemates have been identifying the presence of time points. The dissimilar changes occurs called SEPERATION Change Point Detection. It will not coincide when a remarkable occurrence of events at any points. Change Point Detection (CPD) occurs at the same time and in the problem of finding unexpected changes in facts and statistics collected together for references and analysis and in the property of the time series changes. An unusual real-time not involving any assumptions as to the form or in the parameters of a frequency distribution change point detection algorithm called Separation , It is used to calculate as a parting to recognize change points in fully measurement characteristics relating to measurements having sufficient depth and substance to be in time series. To ameliorate the order of this algorithm used in ARIMA with SEP algorithm. ARIMA model is used for predicting the Time series forecasting result. If emergency is occur then automatically send notification to caring person. The proposed work can decreasing computational cost and also improves the detection accuracy in the quality or fact of being useful of proposed technique.

scholarly journals Bayesian Online Change Point Detection for Baseline Shifts

2020 ◽  
Vol 9 (1) ◽  
pp. 1-16
Author(s):  
Ginga Yoshizawa
Keyword(s):  
Time Series ◽  
Change Point ◽  
Time Series Data ◽  
Synthetic Data ◽  
Change Point Detection ◽  
Series Data ◽  
Data Sets ◽  
Initial State ◽  
Real World Data ◽  
Point Detection

In time series data analysis, detecting change points on a real-time basis (online) is of great interest in many areas, such as finance, environmental monitoring, and medicine. One promising means to achieve this is the Bayesian online change point detection (BOCPD) algorithm, which has been successfully adopted in particular cases in which the time series of interest has a fixed baseline. However, we have found that the algorithm struggles when the baseline irreversibly shifts from its initial state. This is because with the original BOCPD algorithm, the sensitivity with which a change point can be detected is degraded if the data points are fluctuating at locations relatively far from the original baseline. In this paper, we not only extend the original BOCPD algorithm to be applicable to a time series whose baseline is constantly shifting toward unknown values but also visualize why the proposed extension works. To demonstrate the efficacy of the proposed algorithm compared to the original one, we examine these algorithms on two real-world data sets and six synthetic data sets.

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