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. "Clustering Commodity Markets in Space and Time" [DOI: 10.1016/j.resourpol.2021.102162] examined returns and volatility in commodity markets. That paper supported the conventional ontology of commodity markets for precious metals, base metals, agricultural commodities, and crude oil and refined fuels. "A Pattern New in Every Moment" [DOI: 10.3390/en14196099], used temporal clustering to identify critical periods in the trading of crude oil, gasoline, and diesel. This study combines the ontological methodology of "Commodity Markets in Space and Time" with the temporal clustering performed in "A Pattern New in Every Moment." 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 re-lated time series, in finance and beyond