Topology-driven classification of time series (opens in new tab)

Time series analysis is fundamentally limited by the lack of representations that reflect the underlying generative mechanisms of observed signals. Existing approaches, ranging from spectral decompositions to modern machine learning, primarily operate on signal values or frequency content, and therefore fail to capture the intrinsic structure of the dynamics that produce the data. In this work, we introduce a geometric framework that establishes a direct correspondence between the generative ...