Volume 134, Number 5, June 2021
|Number of page(s)||7|
|Published online||10 August 2021|
Time series classification based on detrended partial cross-correlation
School of Science, Beijing Jiaotong University - Beijing 100044, PRC
Received: 20 January 2021
Accepted: 15 April 2021
Classifying stocks by measuring the similarity between them can provide investors with a reliable reference and help them earn more profits than before. This paper attempts to explore a convincing method to measure the similarity of international stocks. We selected the daily closing prices of 18 stocks from the Americas, Asia, Europe, and Australia, and mapped them as points into a three-dimensional space. In order to measure the similarity of stocks, we recommend calculating the Hurst surface distance as a distance matrix to classify stocks through the multidimensional scaling (MDS) method. We compare the classification results with classical MDS using Euclidean distance as similarity measure and MDS based on the (the detrending partial cross-correlation (DPXA) coefficient). The research results show that using Hurst surface distance as a reflection of similarity can not only provide more relevant information, but also distinguish the differences of economic fluctuations in different regions, while lays more emphasis on the similarities and differences within the same region. Both the two improved techniques for MDS are superior to the classic method based on Euclidean distance. In addition, the two methods can provide more detailed and clearer information.
© 2021 EPLA
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.