scholarly journals Water quality monitoring with online change-point detection methods

2014 ◽  
Vol 17 (1) ◽  
pp. 7-19 ◽  
Author(s):  
Amadou Ba ◽  
Sean A. McKenna
Keyword(s):  
Water Quality ◽  
Time Series ◽  
Change Point ◽  
Change Point Detection ◽  
Ar Model ◽  
Detection Methods ◽  
Ratio Test ◽  
Positive Rate ◽  
Point Detection ◽  
Contamination Events

We develop an approach for water quality time series monitoring and contamination event detection. The approach combines affine projection algorithms and an autoregressive (AR) model to predict water quality time series. Then, we apply online change-point detection methods to the estimated residuals to determine the presence, or not, of contamination events. Particularly, we compare the performance of four change-point detection methods, namely, sequential probability ratio test (SPRT), cumulative sum (CUSUM), binomial event discriminator (BED), and online Bayesian change-point detection (OBCPD), by using residuals obtained from four water quality time series, chlorine, conductivity, total organic carbon, and turbidity. Our fundamental criterion for the performance evaluation of the four change-point detection methods is given by the receiver operating characteristic (ROC) curve which is characterized by the true positive rate as a function of the false positive rate. We highlight with detailed experiments that OBCPD provides the best performance for large contamination events, and we also provide insight on the choice of change-point detection algorithms to consider for designing efficient contamination detection schemes.

DEVELOPING TIME-BASED CLUSTERING NEURAL NETWORKS TO USE CHANGE-POINT DETECTION: APPLICATION TO FINANCIAL TIME SERIES

2005 ◽  
Vol 22 (01) ◽  
pp. 51-70 ◽  
Author(s):  
KYONG JOO OH ◽  
TAE HYUP ROH ◽  
MYUNG SANG MOON
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Change Point ◽  
Financial Time Series ◽  
Change Point Detection ◽  
Detection Methods ◽  
Financial Time ◽  
Point Groups ◽  
Point Detection

This study suggests time-based clustering models integrating change-point detection and neural networks, and applies them to financial time series forecasting. The basic concept of the proposed models is to obtain intervals divided by change points, to identify them as change-point groups, and to involve them in the forecasting model. The proposed models consist of two stages. The first stage, the clustering neural network modeling stage, is to detect successive change points in the dataset, and to forecast change-point groups with backpropagation neural networks (BPNs). In this stage, three change-point detection methods are applied and compared. They are: (1) the parametric approach, (2) the nonparametric approach, and (3) the model-based approach. The next stage is to forecast the final output with BPNs. Through the application to financial time series forecasting, we compare the proposed models with a neural network model alone and, in addition, determine which of three change-point detection methods performs better. Furthermore, we evaluate whether the proposed models play a role in clustering to reflect the time. Finally, this study examines the predictability of the integrated neural network models based on change-point detection.

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

2017 ◽  
Author(s):  
Nathan Gold ◽  
Martin G. Frasch ◽  
Christoph Herry ◽  
Bryan S. Richardson ◽  
Xiaogang Wang
Keyword(s):  
Time Series ◽  
Change Point ◽  
Change Point Detection ◽  
Detection Methods ◽  
Series Data ◽  
Physiological Data ◽  
Data Set ◽  
Fetal Sheep ◽  
Robust Statistical Method ◽  
Point Detection

ABSTRACTExperimentally and clinically collected time series data are often contaminated with significant confounding noise, creating short, noisy time series. This noise, due to natural variability and measurement error, poses a challenge to conventional change point detection methods.We propose a novel and robust statistical method for change point detection for noisy biological time sequences. Our method is a significant improvement over traditional change point detection methods, which only examine a potential anomaly at a single time point. In contrast, our method considers all suspected anomaly points and considers the joint probability distribution of the number of change points and the elapsed time between two consecutive anomalies. We validate our method with three simulated time series, a widely accepted benchmark data set, two geological time series, a data set of ECG recordings, and a physiological data set of heart rate variability measurements of fetal sheep model of human labour, comparing it to three existing methods. Our method demonstrates significantly improved performance over the existing pointwise detection methods.

scholarly journals Detecting correlation changes in multivariate time series: A comparison of four non-parametric change point detection methods

2016 ◽  
Vol 49 (3) ◽  
pp. 988-1005 ◽  
Author(s):  
Jedelyn Cabrieto ◽  
Francis Tuerlinckx ◽  
Peter Kuppens ◽  
Mariel Grassmann ◽  
Eva Ceulemans
Keyword(s):  
Time Series ◽  
Change Point ◽  
Multivariate Time Series ◽  
Change Point Detection ◽  
Detection Methods ◽  
Parametric Change ◽  
Point Detection ◽  
Non Parametric

scholarly journals Change Point Detection for Diversely Distributed Stochastic Processes Using a Probabilistic Method

Inventions ◽  
2019 ◽  
Vol 4 (3) ◽  
pp. 42
Author(s):  
Muhammad Rizwan Khan ◽  
Biswajit Sarkar
Keyword(s):  
Time Series ◽  
Likelihood Ratio Test ◽  
Data Structures ◽  
Change Point ◽  
Time Series Data ◽  
Probabilistic Method ◽  
Change Point Detection ◽  
Series Data ◽  
Ratio Test ◽  
Point Detection

Unpredicted deviations in time series data are called change points. These unexpected changes indicate transitions between states. Change point detection is a valuable technique in modeling to estimate unanticipated property changes underlying time series data. It can be applied in different areas like climate change detection, human activity analysis, medical condition monitoring and speech and image analyses. Supervised and unsupervised techniques are equally used to identify changes in time series. Even though change point detection algorithms have improved considerably in recent years, several undefended challenges exist. Previous work on change point detection was limited to specific areas; therefore, more studies are required to investigate appropriate change point detection techniques applicable to any data distribution to assess the numerical productivity of any stochastic process. This research is primarily focused on the formulation of an innovative methodology for change point detection of diversely distributed stochastic processes using a probabilistic method with variable data structures. Bayesian inference and a likelihood ratio test are used to detect a change point at an unknown time (k). The likelihood of k is determined and used in the likelihood ratio test. Parameter change must be evaluated by critically analyzing the parameters expectations before and after a change point. Real-time data of particulate matter concentrations at different locations were used for numerical verification, due to diverse features, that is, environment, population densities and transportation vehicle densities. Therefore, this study provides an understanding of how well this recommended model could perform for different data structures.

CHANGE POINT DETECTION IN TIME SERIES DATA USING SUPPORT VECTORS

2010 ◽  
Vol 24 (01) ◽  
pp. 73-95 ◽  
Author(s):  
FATIH CAMCI
Keyword(s):  
Time Series ◽  
Change Point ◽  
Time Series Data ◽  
Dimensional Space ◽  
Change Point Detection ◽  
Ar Model ◽  
Series Data ◽  
Standard Normal Distribution ◽  
Support Vectors ◽  
Point Detection

Change Point Detection in time series data is of interest in various research areas including data mining, pattern recognition, statistics, etc. Even though there are several effective methods in the literature for detecting changes in mean, and an increase in variance, there are none for decrease in variance. Effective detection of decreased variance has been reported as future work in earlier papers. In addition, most, if not all, methods require some model like AR to fit into the time series data in order to extract noise information, which is assumed to be independent and identically distributed (i.i.d.) and follow standard normal distribution (white noise). Thus, effectiveness of the methods is tied to the fitness degree of the AR model to the time series data. This paper presents a change point detection method based on support vectors that targets changes in mean and variance (including variance decrease) without any assumption of model fitting or data distribution. The data is represented by a hyper-sphere in a higher dimensional space using kernel trick. The change is identified by the change in the radius of the hyper-sphere. A comparison of this method with other methods is presented in the paper.

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