Hints of Earlier and Other Creation: Unsupervised Machine Learning in Financial Time-Series Analysis
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, bas...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-07-01
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| Series: | Engineering Proceedings |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2673-4591/39/1/42 |
| _version_ | 1827726299850866688 |
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| author | James Ming Chen Charalampos Agiropoulos |
| author_facet | James Ming Chen Charalampos Agiropoulos |
| author_sort | James Ming Chen |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T22:48:09Z |
| format | Article |
| id | doaj.art-44d52a4040d5461a9d604b81b4cf02ce |
| institution | Directory Open Access Journal |
| issn | 2673-4591 |
| language | English |
| last_indexed | 2024-03-10T22:48:09Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Engineering Proceedings |
| spelling | doaj.art-44d52a4040d5461a9d604b81b4cf02ce2023-11-19T10:30:51ZengMDPI AGEngineering Proceedings2673-45912023-07-013914210.3390/engproc2023039042Hints of Earlier and Other Creation: Unsupervised Machine Learning in Financial Time-Series AnalysisJames Ming Chen0Charalampos Agiropoulos1College of Law, Michigan State University, East Lansing, MI 48824, USASchool of Economics, Business, and International Studies, University of Piraeus, 18534 Piraeus, GreeceThis 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.https://www.mdpi.com/2673-4591/39/1/42unsupervised machine learningclusteringfinancial time seriescommoditiesenergyfossil fuels |
| spellingShingle | James Ming Chen Charalampos Agiropoulos Hints of Earlier and Other Creation: Unsupervised Machine Learning in Financial Time-Series Analysis Engineering Proceedings unsupervised machine learning clustering financial time series commodities energy fossil fuels |
| title | Hints of Earlier and Other Creation: Unsupervised Machine Learning in Financial Time-Series Analysis |
| title_full | Hints of Earlier and Other Creation: Unsupervised Machine Learning in Financial Time-Series Analysis |
| title_fullStr | Hints of Earlier and Other Creation: Unsupervised Machine Learning in Financial Time-Series Analysis |
| title_full_unstemmed | Hints of Earlier and Other Creation: Unsupervised Machine Learning in Financial Time-Series Analysis |
| title_short | Hints of Earlier and Other Creation: Unsupervised Machine Learning in Financial Time-Series Analysis |
| title_sort | hints of earlier and other creation unsupervised machine learning in financial time series analysis |
| topic | unsupervised machine learning clustering financial time series commodities energy fossil fuels |
| url | https://www.mdpi.com/2673-4591/39/1/42 |
| work_keys_str_mv | AT jamesmingchen hintsofearlierandothercreationunsupervisedmachinelearninginfinancialtimeseriesanalysis AT charalamposagiropoulos hintsofearlierandothercreationunsupervisedmachinelearninginfinancialtimeseriesanalysis |