Aadit Jerfy · 2026 · arXiv (Cornell University)
DOI: 10.48550/arxiv.2610.25756 · cited by 0 works (OpenAlex) · open-access PDF
El análisis de datos topológicos (TDA) y la homología persistente (PH) se han utilizado en numerosos campos desde la modelización de proteínas hasta el análisis financiero para comprender la estructura subyacente de los datos. En este artículo, incorporamos TDA y PH en modelos de predicción en un universo de mercado de predicción para determinar si el estado de creencia de tales mercados forma una estructura subyacente cuya forma puede proporcionar una visión predictiva.
Machine translation (Meta M2M100); the English original follows.
Topological Data Analysis (TDA) and Persistent Homology (PH) have been utilized in numerous fields$-$from protein modeling to financial analysis$-$to understand the underlying structure of data. In this paper, we incorporate TDA and PH into forecasting models over a prediction market universe to determine whether the belief state of such markets forms an underlying structure whose shape can provide predictive insight. We conclude that single-parameter PH is able to improve predictive ability and calibrated accuracy of linear forecasting models, while two-parameter multipersistence produces similar benefits for nonlinear models.
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Topics: Nonlinear system, Computer science, Econometrics, Artificial intelligence, Machine learning, State (computer science)
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