2026-07-23T20:10:31Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:23223 2025-10-02T12:43:32Z ddc:004 open_access

Banana Trees for the Persistence in Time Series Experimentally Ost, Lara Cultrera di Montesano, Sebastiano Edelsbrunner, Herbert persistent homology time series data structures computational experiments In numerous fields, dynamic time series data require continuous updates, necessitating efficient data processing techniques for accurate analysis. This paper examines the banana tree data structure, specifically designed to efficiently maintain the multi-scale topological descriptor commonly known as persistent homology for dynamically changing time series data. We implement this data structure and conduct an experimental study to assess its properties and runtime for update operations. Our findings indicate that banana trees are highly effective with unbiased random data, outperforming state-of-the-art static algorithms in these scenarios. Additionally, our results show that real-world time series share structural properties with unbiased random walks, suggesting potential practical utility for our implementation. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Lara Ost and Sebastiano Cultrera di Montesano and Herbert Edelsbrunner 2025 Is Part Of LIPIcs, Volume 332, 41st International Symposium on Computational Geometry (SoCG 2025) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/LIPIcs.SoCG.2025.71 urn:nbn:de:0030-drops-232237 https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SoCG.2025.71 eng https://creativecommons.org/licenses/by/4.0/legalcode