scholarly journals Change-point detection: application of Cusum method to real life data

2021 ◽  
Vol 8 (1) ◽  
pp. 1041-1047
Author(s):  
Edoh Katchekpele ◽  
Tchilabalo Abozou Kpanzou ◽  
Jean-Etienne Ouindllassida Ouédraogo
Keyword(s):  
Change Point ◽  
Real Life ◽  
Detection Algorithm ◽  
Change Point Detection ◽  
Test Statistic ◽  
Life Data ◽  
Real Life Data ◽  
Abrupt Changes ◽  
Cusum Method ◽  
Point Detection

Several procedures have been developed for the detection of abrupt changes in time series. Among these procedures, it can be mentioned the Cumulative Sum (Cusum) type method. It is in such a perspective that Katchekpele et al. (2017) proposed a method using a Cusum type test to detect a change-point in the unconditional variance of the generalised autoregressive conditional heteroskedasticity(GARCH) models. The aim of this paper is to present an application of their technique. After briefly recalling how the test statistic was constructed, the change-point detection algorithm is given and it is shown how it is applied to some real life data.

Unsupervised Stress Detection Algorithm and Experiments with Real Life Data

2017 ◽  
pp. 95-107 ◽  
Author(s):  
Elena Vildjiounaite ◽  
Johanna Kallio ◽  
Jani Mäntyjärvi ◽  
Vesa Kyllönen ◽  
Mikko Lindholm ◽  
...  
Keyword(s):  
Real Life ◽  
Detection Algorithm ◽  
Stress Detection ◽  
Life Data ◽  
Real Life Data

scholarly journals A Doubly Stochastic Change Point Detection Algorithm for Noisy Biological Signals

2018 ◽  
Vol 8 ◽  
Author(s):  
Nathan Gold ◽  
Martin G. Frasch ◽  
Christophe L. Herry ◽  
Bryan S. Richardson ◽  
Xiaogang Wang
Keyword(s):  
Change Point ◽  
Detection Algorithm ◽  
Change Point Detection ◽  
Doubly Stochastic ◽  
Biological Signals ◽  
Point Detection

scholarly journals TESTING FOR CHANGES IN KENDALL’S TAU

2016 ◽  
Vol 33 (6) ◽  
pp. 1352-1386 ◽  
Author(s):  
Herold Dehling ◽  
Daniel Vogel ◽  
Martin Wendler ◽  
Dominik Wied
Keyword(s):  
Change Point ◽  
Heavy Tails ◽  
High Efficiency ◽  
Real Life ◽  
Test Statistic ◽  
Kendall’S Tau ◽  
Kendall's Tau ◽  
Heavy Tailed Distributions ◽  
Real Life Data ◽  
Heavy Tailed

For a bivariate time series ((Xi ,Yi))i=1,...,n, we want to detect whether the correlation between Xi and Yi stays constant for all i = 1,...n. We propose a nonparametric change-point test statistic based on Kendall’s tau. The asymptotic distribution under the null hypothesis of no change follows from a new U-statistic invariance principle for dependent processes. Assuming a single change-point, we show that the location of the change-point is consistently estimated. Kendall’s tau possesses a high efficiency at the normal distribution, as compared to the normal maximum likelihood estimator, Pearson’s moment correlation. Contrary to Pearson’s correlation coefficient, it shows no loss in efficiency at heavy-tailed distributions, and is therefore particularly suited for financial data, where heavy tails are common. We assume the data ((Xi ,Yi))i=1,...,n to be stationary and P-near epoch dependent on an absolutely regular process. The P-near epoch dependence condition constitutes a generalization of the usually considered Lp-near epoch dependence allowing for arbitrarily heavy-tailed data. We investigate the test numerically, compare it to previous proposals, and illustrate its application with two real-life data examples.

scholarly journals Machine Committee Framework for Power Grid Disturbances Analysis Using Synchrophasors Data

Smart Cities ◽  
2020 ◽  
Vol 4 (1) ◽  
pp. 1-16
Author(s):  
Haoran Niu ◽  
Olufemi A. Omitaomu ◽  
Qing C. Cao
Keyword(s):  
Event Detection ◽  
Change Point ◽  
Power Grid ◽  
Detection Algorithm ◽  
Change Point Detection ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Disturbance Analysis ◽  
Events Detection ◽  
Point Detection

Events detection is a key challenge in power grid frequency disturbances analysis. Accurate detection of events is crucial for situational awareness of the power system. In this paper, we study the problem of events detection in power grid frequency disturbance analysis using synchrophasors data streams. Current events detection approaches for power grid rely on individual detection algorithm. This study integrates some of the existing detection algorithms using the concept of machine committee to develop improved detection approaches for grid disturbance analysis. Specifically, we propose two algorithms—an Event Detection Machine Committee (EDMC) algorithm and a Change-Point Detection Machine Committee (CPDMC) algorithm. Both algorithms use parallel architecture to fuse detection knowledge of its individual methods to arrive at an overall output. The EDMC algorithm combines five individual event detection methods, while the CPDMC algorithm combines two change-point detection methods. Each method performs the detection task separately. The overall output of each algorithm is then computed using a voting strategy. The proposed algorithms are evaluated using three case studies of actual power grid disturbances. Compared with the individual results of the various detection methods, we found that the EDMC algorithm is a better fit for analyzing synchrophasors data; it improves the detection accuracy; and it is suitable for practical scenarios.

scholarly journals Real-time particle filtering and smoothing algorithms for detecting abrupt changes in neural ensemble spike activity

2018 ◽  
Vol 119 (4) ◽  
pp. 1394-1410 ◽  
Author(s):  
Sile Hu ◽  
Qiaosheng Zhang ◽  
Jing Wang ◽  
Zhe Chen
Keyword(s):  
Real Time ◽  
Spike Activity ◽  
Change Point ◽  
Particle Filtering ◽  
Jump Process ◽  
Change Point Detection ◽  
Stochastic Jump ◽  
Abrupt Changes ◽  
Non Gaussian ◽  
Point Detection

Sequential change-point detection from time series data is a common problem in many neuroscience applications, such as seizure detection, anomaly detection, and pain detection. In our previous work (Chen Z, Zhang Q, Tong AP, Manders TR, Wang J. J Neural Eng 14: 036023, 2017), we developed a latent state-space model, known as the Poisson linear dynamical system, for detecting abrupt changes in neuronal ensemble spike activity. In online brain-machine interface (BMI) applications, a recursive filtering algorithm is used to track the changes in the latent variable. However, previous methods have been restricted to Gaussian dynamical noise and have used Gaussian approximation for the Poisson likelihood. To improve the detection speed, we introduce non-Gaussian dynamical noise for modeling a stochastic jump process in the latent state space. To efficiently estimate the state posterior that accommodates non-Gaussian noise and non-Gaussian likelihood, we propose particle filtering and smoothing algorithms for the change-point detection problem. To speed up the computation, we implement the proposed particle filtering algorithms using advanced graphics processing unit computing technology. We validate our algorithms, using both computer simulations and experimental data for acute pain detection. Finally, we discuss several important practical issues in the context of real-time closed-loop BMI applications. NEW & NOTEWORTHY Sequential change-point detection is an important problem in closed-loop neuroscience experiments. This study proposes novel sequential Monte Carlo methods to quickly detect the onset and offset of a stochastic jump process that drives the population spike activity. This new approach is robust with respect to spike sorting noise and varying levels of signal-to-noise ratio. The GPU implementation of the computational algorithm allows for parallel processing in real time.

A cusum change-point detection algorithm for non-stationary sequences with application to data network surveillance

2010 ◽  
Vol 83 (7) ◽  
pp. 1288-1297 ◽  
Author(s):  
Veronica Montes De Oca ◽  
Daniel R. Jeske ◽  
Qi Zhang ◽  
Carlos Rendon ◽  
Mazda Marvasti
Keyword(s):  
Change Point ◽  
Detection Algorithm ◽  
Change Point Detection ◽  
Stationary Sequences ◽  
Data Network ◽  
Network Surveillance ◽  
Point Detection
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