Tony Bagnall
Tony is a Professor of Computer Science at the University of East Anglia, where he leads the time series machine learning group. His primary research interest is in time series machine learning, with a historic focus on classification, but more recently looking at clustering and regression. He has a side interest in ensemble design.
Sessions
Many algorithms for machine learning from time series are based on measuring the distance or similarity between series. The most popular distance measure is dynamic time warping, which attempts to optimally realign two series to compensate for offest. There are many others though. We present an overview of the most popular time series specific distance functions and describe their speed optimised implementations in aeon, a scikit-learn compatible time series machine learning toolkit. We demonstrate their application for clustering, classification and regression on a real world case study and highlight some of the latest distance based time series machine learning tools available in aeon.