Newest papers

Topological Compression of Prediction Market Belief States for Event Forecasting

Aadit Jerfy · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.25756 · cited by 0 works (OpenAlex) · open-access PDF

Chinese

Topological Data Analysis (TDA) 和 Persistent Homology (PH) 已被用于许多领域,从蛋白质建模到财务分析,以了解数据的底层结构. 在本文中,我们将 TDA 和 PH 纳入预测市场宇宙的预测模型中,以确定这些市场的信念状态是否形成了一个底层结构,其形状可以提供预测见解。

Machine translation (Meta M2M100); the English original follows.

Abstract

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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