Applying Grey Relational Analysis to Detect Change Points in Time Series

The goal of detecting change points is to recognize abrupt changes in time series data. This is suitable, for instance, to find events that characterize the financial market or to inspect data streams of stock returns. Regression models categorized as supervised methods have played a significant rol...

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Bibliographic Details
Published in:Journal of mathematics (Hidawi) 2022, Vol.2022 (1)
Main Authors: Hu, Yi-Chung, Chiang, Shu-hen, Chiu, Yu-Jing
Format: Article
Language:English
Subjects:
Artificial intelligence
Content analysis
Data transmission
Hydrology
Mathematics
Methods
Performance evaluation
Regression models
Sequences
Statistical analysis
Time series
Variables
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Summary:The goal of detecting change points is to recognize abrupt changes in time series data. This is suitable, for instance, to find events that characterize the financial market or to inspect data streams of stock returns. Regression models categorized as supervised methods have played a significant role in change-point detection. However, since change points might not be available beforehand to train the model, and because the series data might be statistically atypical, the applicability of regression models is limited. To avoid statistical assumptions, this study uses the grey theory, a kind of artificial intelligence tools, to measure the relationships between sequences by grey relational analysis (GRA). This paper contributes to propose an unsupervised method to detect possible change points in time series by GRA. Change-point analysis of the proposed method was performed on S&P100 stock returns. Experimental results from evaluating the recognition accuracy rate show that the proposed method performs well compared to other methods considered for change-point detection.
ISSN:2314-4629
2314-4785
DOI:10.1155/2022/9242773