Interpretable Classification of Time Series Using Euler Characteristic Surfaces

ArXi:2603.15079v1 Announce Type: new Persistent homology (PH) -- the conventional method in topological data analysis -- is computationally expensive, requires further vectorization of its signatures before machine learning (ML) can be applied, and captures information along only the spatial axis. For time series data, we propose Euler Characteristic Surfaces (ECS) as an alternative topological signature based on the Euler characteristic ($\chi$) -- a fundamental topological invariant.