Missing data estimation method for time series data in structure health monitoring systems by probability principal component analysis

2020 ◽  
Vol 149 ◽  
pp. 102901
Author(s):  
Linchao Li ◽  
Hanlin Liu ◽  
Haijun Zhou ◽  
Chaodong Zhang
Keyword(s):  
Principal Component Analysis ◽  
Health Monitoring ◽  
Time Series Data ◽  
Estimation Method ◽  
Principal Component ◽  
Series Data ◽  
Monitoring Systems ◽  
Health Monitoring Systems ◽  
Missing Data Estimation ◽  
Data Estimation

Support Vector Regression for Missing Data Estimation

2010 ◽  
pp. 117-141
Author(s):  
Tshilidzi Marwala
Keyword(s):  
Principal Component Analysis ◽  
Support Vector Regression ◽  
Principal Component ◽  
Component Analysis ◽  
Support Vector ◽  
Overall Performance ◽  
Accuracy Measures ◽  
Missing Variables ◽  
Missing Data Estimation ◽  
Data Estimation

This chapter develops and compares the merits of three different data imputation models by using accuracy measures. The three methods are auto-associative neural networks, a principal component analysis and support vector regression all combined with cultural genetic algorithms to impute missing variables. The use of a principal component analysis improves the overall performance of the auto-associative network while the use of support vector regression shows promising potential for future investigation. Imputation accuracies up to 97.4% for some of the variables are achieved.

scholarly journals RESEARCH ON THE RELATIONSHIP BETWEEN INVESTOR SENTIMENT AND STOCK PRICE

2021 ◽  
Author(s):  
YI-MING DU ◽  
RUI DING ◽  
YI-LIN ZHANG ◽  
TING ZHANG ◽  
TAO ZHOU
Keyword(s):  
Principal Component Analysis ◽  
Behavioral Finance ◽  
Stock Price ◽  
Time Series Data ◽  
Investor Sentiment ◽  
Principal Component ◽  
Component Analysis ◽  
Series Data ◽  
Macroeconomic Factors ◽  
The Relationship

As one of the main contents of behavioral finance, investor sentiment has become a research hotspot in recent years. This paper takes the CSI300 index of China as the observation object, selects five emotional monthly time series data including lag one period from 2016 to 2020. The method of principal component analysis will be used to reduce the dimension of 10 groups of data. After eliminating the macroeconomic factors, the dimension reduction results are analyzed by the second principal component analysis to obtain the comprehensive index of emotion. Furthermore, a Vector Auto Regressive model (VAR) is established to investigate the relationship between ISIO and CSI300 of the stock market. The results show that investor sentiment and stock price interact with each other, but only in the short term. With more and more sufficient market information known, the effect is becoming insignificant.

scholarly journals Symplectic Principal Component Analysis: A New Method for Time Series Analysis

2011 ◽  
Vol 2011 ◽  
pp. 1-14 ◽  
Author(s):  
Min Lei ◽  
Guang Meng
Keyword(s):  
Dynamical System ◽  
Principal Component Analysis ◽  
Time Series ◽  
Symplectic Geometry ◽  
Time Series Data ◽  
Principal Component ◽  
Component Analysis ◽  
Series Data ◽  
Pca Method ◽  
Value Decomposition

Experimental data are often very complex since the underlying dynamical system may be unknown and the data may heavily be corrupted by noise. It is a crucial task to properly analyze data to get maximal information of the underlying dynamical system. This paper presents a novel principal component analysis (PCA) method based on symplectic geometry, called symplectic PCA (SPCA), to study nonlinear time series. Being nonlinear, it is different from the traditional PCA method based on linear singular value decomposition (SVD). It is thus perceived to be able to better represent nonlinear, especially chaotic data, than PCA. Using the chaotic Lorenz time series data, we show that this is indeed the case. Furthermore, we show that SPCA can conveniently reduce measurement noise.

scholarly journals Moving dynamic principal component analysis for non-stationary multivariate time series

2021 ◽  
Author(s):  
Fayed Alshammri ◽  
Jiazhu Pan
Keyword(s):  
Principal Component Analysis ◽  
Time Series ◽  
Time Series Data ◽  
Multivariate Time Series ◽  
Principal Component ◽  
Real Data ◽  
Component Analysis ◽  
Series Data ◽  
Dynamic Principal Component Analysis ◽  
Dynamic Relationships

AbstractThis paper proposes an extension of principal component analysis to non-stationary multivariate time series data. A criterion for determining the number of final retained components is proposed. An advance correlation matrix is developed to evaluate dynamic relationships among the chosen components. The theoretical properties of the proposed method are given. Many simulation experiments show our approach performs well on both stationary and non-stationary data. Real data examples are also presented as illustrations. We develop four packages using the statistical software R that contain the needed functions to obtain and assess the results of the proposed method.

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