PhD Title: Machine Learning with Time Series
Abstract: Markus’ research is on supervised learning with time series/panel data, i.e. observations on multiple independent individuals (e.g. customers, patients or machines) collected repeatedly over time. His research aims to:
- Create a unified, practical and statistically solid workflow for modelling and evaluating supervised learning strategies with time series/panel data,
- Design and implement an open-source Python toolbox that allows to put the workflow into practice,
- Develop probabilistic supervised learning methods based on point process models for panel data with sequences of events with exact timestamps,
- Apply workflow to real-world datasets through ongoing industry collaborations, including the loyalty-card data from a UK high-street retailer (via the Consumer Data Research Centre) among others.
Markus has completed an Enrichment Scheme at The Alan Turing Institute. He is supervised by Professor Paul Longley, Dr Franz Király and Professor James Cheshire. Previously he studied Philosophy & Economics at Bayreuth University (Germany) and Lancaster University. Markus is also a contributor to sktime: a scikit-learn compatible Python toolbox for learning with time series data.