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

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 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.

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
Sign in / Sign up

Export Citation Format

Share Document