A Novel Time-Sensitive Composite Similarity Model for Multivariate Time-Series Correlation Analysis
Finding the correlation between stocks is an effective method for screening and adjusting investment portfolios for investors. One single temporal feature or static nontemporal features are generally used in most studies to measure the similarity between stocks. However, these features are not suffi...
Main Authors: | , , |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2021-06-01
|
Series: | Entropy |
Subjects: | |
Online Access: | https://www.mdpi.com/1099-4300/23/6/731 |
_version_ | 1797530840634228736 |
---|---|
author | Mengxia Liang Xiaolong Wang Shaocong Wu |
author_facet | Mengxia Liang Xiaolong Wang Shaocong Wu |
author_sort | Mengxia Liang |
collection | DOAJ |
description | Finding the correlation between stocks is an effective method for screening and adjusting investment portfolios for investors. One single temporal feature or static nontemporal features are generally used in most studies to measure the similarity between stocks. However, these features are not sufficient to explore phenomena such as price fluctuations similar in shape but unequal in length which may be caused by multiple temporal features. To research stock price volatilities entirely, mining the correlation between stocks should be considered from the point view of multiple features described as time series, including closing price, etc. In this paper, a time-sensitive composite similarity model designed for multivariate time-series correlation analysis based on dynamic time warping is proposed. First, a stock is chosen as the benchmark, and the multivariate time series are segmented by the peaks and troughs time-series segmentation (PTS) algorithm. Second, similar stocks are screened out by similarity. Finally, the rate of rising or falling together between stock pairs is used to verify the proposed model’s effectiveness. Compared with other models, the composite similarity model brings in multiple temporal features and is generalizable for numerical multivariate time series in different fields. The results show that the proposed model is very promising. |
first_indexed | 2024-03-10T10:35:44Z |
format | Article |
id | doaj.art-022abddaf9a44045964ae002fc88aef0 |
institution | Directory Open Access Journal |
issn | 1099-4300 |
language | English |
last_indexed | 2024-03-10T10:35:44Z |
publishDate | 2021-06-01 |
publisher | MDPI AG |
record_format | Article |
series | Entropy |
spelling | doaj.art-022abddaf9a44045964ae002fc88aef02023-11-21T23:19:12ZengMDPI AGEntropy1099-43002021-06-0123673110.3390/e23060731A Novel Time-Sensitive Composite Similarity Model for Multivariate Time-Series Correlation AnalysisMengxia Liang0Xiaolong Wang1Shaocong Wu2College of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, ChinaCollege of Computer Science and Technology, Harbin Institute of Technology, Shenzhen 518055, ChinaCollege of Computer Science and Technology, Harbin Institute of Technology, Shenzhen 518055, ChinaFinding the correlation between stocks is an effective method for screening and adjusting investment portfolios for investors. One single temporal feature or static nontemporal features are generally used in most studies to measure the similarity between stocks. However, these features are not sufficient to explore phenomena such as price fluctuations similar in shape but unequal in length which may be caused by multiple temporal features. To research stock price volatilities entirely, mining the correlation between stocks should be considered from the point view of multiple features described as time series, including closing price, etc. In this paper, a time-sensitive composite similarity model designed for multivariate time-series correlation analysis based on dynamic time warping is proposed. First, a stock is chosen as the benchmark, and the multivariate time series are segmented by the peaks and troughs time-series segmentation (PTS) algorithm. Second, similar stocks are screened out by similarity. Finally, the rate of rising or falling together between stock pairs is used to verify the proposed model’s effectiveness. Compared with other models, the composite similarity model brings in multiple temporal features and is generalizable for numerical multivariate time series in different fields. The results show that the proposed model is very promising.https://www.mdpi.com/1099-4300/23/6/731dynamic time warpingtime-series segmentationtime-series correlationtemporal features |
spellingShingle | Mengxia Liang Xiaolong Wang Shaocong Wu A Novel Time-Sensitive Composite Similarity Model for Multivariate Time-Series Correlation Analysis Entropy dynamic time warping time-series segmentation time-series correlation temporal features |
title | A Novel Time-Sensitive Composite Similarity Model for Multivariate Time-Series Correlation Analysis |
title_full | A Novel Time-Sensitive Composite Similarity Model for Multivariate Time-Series Correlation Analysis |
title_fullStr | A Novel Time-Sensitive Composite Similarity Model for Multivariate Time-Series Correlation Analysis |
title_full_unstemmed | A Novel Time-Sensitive Composite Similarity Model for Multivariate Time-Series Correlation Analysis |
title_short | A Novel Time-Sensitive Composite Similarity Model for Multivariate Time-Series Correlation Analysis |
title_sort | novel time sensitive composite similarity model for multivariate time series correlation analysis |
topic | dynamic time warping time-series segmentation time-series correlation temporal features |
url | https://www.mdpi.com/1099-4300/23/6/731 |
work_keys_str_mv | AT mengxialiang anoveltimesensitivecompositesimilaritymodelformultivariatetimeseriescorrelationanalysis AT xiaolongwang anoveltimesensitivecompositesimilaritymodelformultivariatetimeseriescorrelationanalysis AT shaocongwu anoveltimesensitivecompositesimilaritymodelformultivariatetimeseriescorrelationanalysis AT mengxialiang noveltimesensitivecompositesimilaritymodelformultivariatetimeseriescorrelationanalysis AT xiaolongwang noveltimesensitivecompositesimilaritymodelformultivariatetimeseriescorrelationanalysis AT shaocongwu noveltimesensitivecompositesimilaritymodelformultivariatetimeseriescorrelationanalysis |