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This paper proposes biarchetype analysis, an unsupervised methodology for univariate functional data that identifies extreme representative patterns across cases and time points. While the application focuses on macroeconomic financial time series like government bond yields, the core contribution is a novel statistical technique extending archetype analysis to enhance interpretability in complex datasets. The work represents significant progress in machine learning methodologies specifically designed for analyzing temporal structures within economic variables.
Biarchetype analysis for univariate functional data. An application to macroeconomic financial time series
arXiv:2606.15881v1 Announce Type: cross Abstract: We introduce biarchetype analysis for the first time in the context of univariate functional data. This unsupervised methodology extends archetype analysis by simultaneously identifying archetypal structures across both the cases (countries, in our application) and the temporal argument. Both cases and time points are expressed as mixtures of biarchetypes, yielding a concise and highly interpretable representation of complex functional observations. Although biarchetype analysis is not intended as a clustering technique, it offers superior interpretability compared with biclustering approaches, as it is based on extreme, representative patterns rather than average centroids, thereby enhancing human comprehension. We apply the proposed method to 10-year government bond yields of European countries over the period 2001-2025. The results identify three distinct time regimes (the pre-crisis period, the euro-area sovereign debt crisis, and the post-crisis period), and reveal Germany, Greece, and Hungary as country archetypes.
Title:Biarchetype analysis for univariate functional data. An application to macroeconomic financial time series
Abstract:We introduce biarchetype analysis for the first time in the context of univariate functional data. This unsupervised methodology extends archetype analysis by simultaneously identifying archetypal structures across both the cases (countries, in our application) and the temporal argument. Both cases and time points are expressed as mixtures of biarchetypes, yielding a concise and highly interpretable representation of complex functional observations. Although biarchetype analysis is not intended as a clustering technique, it offers superior interpretability compared with biclustering approaches, as it is based on extreme, representative patterns rather than average centroids, thereby enhancing human comprehension. We apply the proposed method to 10-year government bond yields of European countries over the period 2001-2025. The results identify three distinct time regimes (the pre-crisis period, the euro-area sovereign debt crisis, and the post-crisis period), and reveal Germany, Greece, and Hungary as country archetypes.
6 pages, 2 figures. To be published in the proceedings of SIS-FENStatS 2026, Sapienza University of Rome, Italy, June 22-25, 2026
Methodology (stat.ME); Machine Learning (cs.LG); Applications (stat.AP)
arXiv:2606.15881 [stat.ME]
(or arXiv:2606.15881v1 [stat.ME] for this version)
https://doi.org/10.48550/arXiv.2606.15881 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 271 words
