Hints of Earlier and Other Creation: Unsupervised Machine Learning in Financial Time-Series Analysis

Engineering Proceedings

article

James Ming Chen¹  Charalampos Agiropoulos² 

[1] College of Law, Michigan State University;School of Economics, Business, and International Studies, University of Piraeus

关键词: unsupervised machine learning;    clustering;    financial time series;    commodities;    energy;    fossil fuels;   

DOI  :  10.3390/engproc2023039042

来源: mdpi

【 摘 要 】

This study extends previous work applying unsupervised machine learning to commodity markets. The first article in this sequence examined returns and volatility in commodity markets. The clustering of these time series supported the conventional ontology of commodity markets for precious metals, base metals, agricultural commodities, and crude oil and refined fuels. A second article used temporal clustering to identify critical periods in the trading of crude oil, gasoline, and diesel. This study combines the ontological clustering of financial time series with the temporal clustering of the matrix transpose. Ontological clustering, contingent upon the identification of structural breaks and other critical periods within financial time series, is this study’s distinctive contribution. Conditional, time-variant ontological clustering should be applicable to any set of related time series, in finance and beyond.

【 授权许可】

Unknown