Multi-stage change-point detection scheme for large-scale simultaneous events

2011 ◽  
Vol 34 (15) ◽  
pp. 1810-1821
Author(s):  
Yukinobu Fukushima ◽  
Tutomu Murase ◽  
Masayoshi Kobayashi ◽  
Hiroki Fujiwara ◽  
Ryohei Fujimaki ◽  
...  
Keyword(s):  
Change Point ◽  
Large Scale ◽  
Change Point Detection ◽  
Detection Scheme ◽  
Multi Stage ◽  
Stage Change ◽  
Point Detection

scholarly journals Multi-Stage Change Point Detection with Copula Conditional Distribution with PCA and Functional PCA

Mathematics ◽  
2020 ◽  
Vol 8 (10) ◽  
pp. 1777
Author(s):  
Jong-Min Kim ◽  
Ning Wang ◽  
Yumin Liu
Keyword(s):  
Change Point ◽  
Manufacturing Industry ◽  
Supply And Demand ◽  
Multivariate Data ◽  
Change Point Detection ◽  
Conditional Distributions ◽  
Multi Stage ◽  
Functional Pca ◽  
Point Detection ◽  
Uncertainty Environment

A global uncertainty environment, such as the COVID-19 pandemic, has affected the manufacturing industry severely in terms of supply and demand balancing. So, it is common that one stage statistical process control (SPC) chart affects the next-stage SPC chart. It is our research objective to consider a conditional case for the multi-stage multivariate change point detection (CPD) model for highly correlated multivariate data via copula conditional distributions with principal component analysis (PCA) and functional PCA (FPCA). First of all, we review the current available multivariate CPD models, which are the energy test-based control chart (ETCC) and the nonparametric multivariate change point model (NPMVCP). We extend the current available CPD models to the conditional multi-stage multivariate CPD model via copula conditional distributions with PCA for linear normal multivariate data and FPCA for nonlinear non-normal multivariate data.

scholarly journals Change Point Determination in Audio Data Using Auditory Features

2015 ◽  
Vol 61 (2) ◽  
pp. 185-190 ◽  
Author(s):  
Tomasz Maka
Keyword(s):  
Change Point ◽  
Feature Space ◽  
Change Point Detection ◽  
Detection Scheme ◽  
Detection Techniques ◽  
Detection Process ◽  
Feature Spaces ◽  
Audio Data ◽  
A New Technique ◽  
Point Detection

Abstract The study is aimed to investigate the properties of auditory-based features for audio change point detection process. In the performed analysis, two popular techniques have been used: a metric-based approach and the ΔBIC scheme. The efficiency of the change point detection process depends on the type and size of the feature space. Therefore, we have compared two auditory-based feature sets (MFCC and GTEAD) in both change point detection schemes. We have proposed a new technique based on multiscale analysis to determine the content change in the audio data. The comparison of the two typical change point detection techniques with two different feature spaces has been performed on the set of acoustical scenes with single change point. As the results show, the accuracy of the detected positions depends on the feature type, feature space dimensionality, detection technique and the type of audio data. In case of the ΔBIC approach, the better accuracy has been obtained for MFCC feature space in the most cases. However, the change point detection with this feature results in a lower detection ratio in comparison to the GTEAD feature. Using the same criteria as for ΔBIC, the proposed multiscale metric-based technique has been executed. In such case, the use of the GTEAD feature space has led to better accuracy. We have shown that the proposed multiscale change point detection scheme is competitive to the ΔBIC scheme with the MFCC feature space.

CPMAN: Change Point Detection Approach in Time Series Based on the Prediction of Multi-stage Attention Networks

2021 ◽  
Vol 30 (05) ◽  
pp. 2150026
Author(s):  
Haizhou Du ◽  
Ziyi Duan ◽  
Yang Zheng
Keyword(s):  
Time Series ◽  
Change Point ◽  
Early Stage ◽  
Change Point Detection ◽  
Change Points ◽  
Series Data ◽  
Attention Networks ◽  
Multi Stage ◽  
Detection Approach ◽  
Point Detection

Time series change point detection can identify the locations of abrupt points in many dynamic processes. It can help us to find anomaly data in an early stage. At the same time, detecting change points for long, periodic, and multiple input series data has received a lot of attention recently, and is universally applicable in many fields including power, environment, finance, and medicine. However, the performance of classical methods typically scales poorly for such time series. In this paper, we propose CPMAN, a novel prediction-based change point detection approach via multi-stage attention networks. Our model consists of two key modules. First, in the time series prediction module, we employ the multi-stage attention-based networks and integrate the multi-series fusion mechanism. This module can adaptively extract features from the relevant input series and capture the long-term temporal dependencies. Secondly, in the change point detection module, we use the wavelet analysis-based algorithm to detect change points efficiently and identify the change points and outliers. Extensive experiments are conducted on various real-world datasets and synthetic datasets, proving the superiority and effectiveness of CPMAN. Our approach outperforms the state-of-the-art methods by up to 12.1% on the F1 Score.

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