  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 persistent homology -- a multi-scale topological
descriptor -- 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.
