Applications of Computational Topology to Time Series Analysis

11:30-11:55, January 28 @ 1A

Talk/ Overview

Topology is an abstract measure of structure that quantifies features like number of connected components and holes in data. These features are useful to compare time-series obtained from distinct nonlinear and complex dynamical systems, and even do membership testing or change-point detection. However, TDA is computationally expensive and involves a number of free parameters. The parametric dependence gives rise to the concept of persistent homology: how the shape of the data set changes with the resolution with which it is viewed. We demonstrate how TDA allows us to distinguish time-series data from different systems - here, the same note played on different musical instruments and different regimes of speech during of ECOG recording.

Talk/ Speakers

Nicole Sanderson

Postdoctoral Scholar, LBNL

AMLD / Global partners