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 detection for compositional multivariate data

2021 ◽  
Author(s):  
Prabuchandran K. J. ◽  
Nitin Singh ◽  
Pankaj Dayama ◽  
Ashutosh Agarwal ◽  
Vinayaka Pandit
Keyword(s):  
Change Point ◽  
Multivariate Data ◽  
Change Point Detection ◽  
Point Detection

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.

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

Manufacturing Process Monitoring With Nonparametric Change-Point Detection in Automotive Industry

2019 ◽  
Vol 141 (7) ◽  
Author(s):  
Shenghan Guo ◽  
Weihong (Grace) Guo ◽  
Amir Abolhassani ◽  
Rajeev Kalamdani ◽  
Saumuy Puchala ◽  
...  
Keyword(s):  
Change Point ◽  
Manufacturing Process ◽  
Manufacturing Industry ◽  
Systematic Approach ◽  
Decision Rules ◽  
Change Point Detection ◽  
Detection Methods ◽  
Automotive Manufacturing ◽  
Binary Segmentation ◽  
Point Detection

Automatic sensing devices and computer systems have been widely adopted by the automotive manufacturing industry, which are capable to record machine status and process parameters nonstop. While a manufacturing process always has natural variations, it is crucial to detect significant changes to the process for quality control, as such changes may be the early signs of machine faults. This motivates our study on change-point detection methods for automotive manufacturing. We aim at developing a systematic approach for detecting process changes retrospectively in complex, nonstationary data. The proposed approach consists of nonparametric change-point detection, alarm generation based on change-point estimations, and performance evaluation against historical maintenance records. For change-point detection, three nonparametric methods are suggested—least absolute shrinkage and selection operator (LASSO), thresholded LASSO, and wild binary segmentation (WBS). Multiple decision rules are proposed to determine how to generate alarms from change-point estimations. Numerical studies are conducted to demonstrate the performance of the proposed systematic approach. The different change-point detection methods and different decision rules are evaluated and compared, with scenarios for choosing one set of change-point detection method and decision rule over another combination identified. It is shown that LASSO and thresholded-LASSO outperform WBS when the shift size is small, but WBS produces a smaller false alarm rate and handles the clustering of changes better than LASSO or thresholded LASSO. Data from an automotive manufacturing plant are used in the case study to demonstrate the proposed approach. Guidelines for implementation are also provided.

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