PHINN: Persistent Homology Inspired Neural Network for Rare-Event Time Series Generation

Abstract:Rare events in time series are critical to model but hard to learn due to data scarcity. Current generative models struggle with extreme values. We observe that rare events leave distinct topological fingerprints - transitions in Betti numbers from point-cloud embeddings - that are more stable and discriminative than statistical moments. We introduce PHINN, a flow-matching framework using dynamic Betti curves as conditioning signals and a persistence landscape loss for homology consistency. It scales to multivariate data, includes a natural-language interface to set Betti targets, supports cross-domain meta-learning and few-shot generation, and provides certified adversarial robustness. On financial, epidemiological, and multi-modal benchmarks, PHINN outperforms statistical and diffusion baselines in topological fidelity (beta-RMSE down 41-63%, transition accuracy up 84%) and matches jump-diffusion models in tail coverage while exceeding them in shape fidelity. All results have 95% confidence intervals.

Machine Learning (cs.LG); Algebraic Topology (math.AT); Risk Management (q-fin.RM); Machine Learning (stat.ML)

arXiv:2606.15452 [cs.LG]

(or arXiv:2606.15452v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2606.15452 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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