A Hands-on Introduction to Time Series Classification and Regression
Anthony Bagnall;Matthew Middlehurst;Germain Forestier;Ali Ismail-Fawaz;Antoine Guillaume;David Guijo-Rubio;Chang Wei Tan;Angus Dempster;Geoffrey I. Webb (2024)
Biography
Matthew Middlehurst is a Lecturer of Computer Science from the School of Computing and Engineering, part of the Faculty of Management, Sciences and Engineering at the University of Bradford. He received his B.Sc. in Computer Science and defended his Ph.D. thesis titled "Detection of nuisance call centres using improved hybrid time series classification algorithms" at the University of East Anglia. Prior to joining as a lecturer at Bradford, he worked as a Research Fellow at the University of Southampton.
Matthew’s research focuses on time series machine learning (TSML), a field dedicated to developing methods that learn from sequences of ordered numerical data. Time series problems are central to many areas of science and technology. For instance, real-world applications such as human activity recognition, rehabilitation, digital health (e.g. EEG and ECG), spam call detection, financial analysis, resource management and many others can be approached through TSML methods.
Research
Machine learning
Time series classification and regression
Publications
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Extracting Features from Random Subseries: A Hybrid Pipeline for Time Series Classification and Extrinsic Regression
Matthew Middlehurst;Anthony Bagnall (2023) Lecture Notes in Computer Science.
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The FreshPRINCE: A Simple Transformation Based Pipeline Time Series Classifier
Middlehurst, M.;Bagnall, A. (2022) Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics.
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The Temporal Dictionary Ensemble (TDE) Classifier for Time Series Classification
Middlehurst, M.;Large, J.;Cawley, G.;Bagnall, A. (2021) Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics.
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The Canonical Interval Forest (CIF) Classifier for Time Series Classification
Middlehurst, M.;Large, J.;Bagnall, A. (2020) Proceedings 2020 IEEE International Conference on Big Data Big Data 2020.
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On the usage and performance of the hierarchical vote collective of transformation-based ensembles version 1.0 (HIVE-COTE v1.0)
Bagnall, A.;Flynn, M.;Large, J.;Lines, J.;Middlehurst, M. (2020) Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics.
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Scalable Dictionary Classifiers for Time Series Classification
Matthew Middlehurst;William Vickers;Anthony Bagnall (2019) Lecture Notes in Computer Science.
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A review and evaluation of elastic distance functions for time series clustering
Christopher Holder;Matthew Middlehurst;Anthony Bagnall (2024) Knowledge and Information Systems.
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Bake off redux: a review and experimental evaluation of recent time series classification algorithms
Middlehurst, M.;Schäfer, P.;Bagnall, A. (2024) Data Mining and Knowledge Discovery.
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Unsupervised feature based algorithms for time series extrinsic regression
David Guijo-Rubio;Matthew Middlehurst;Guilherme Arcencio;Diego Furtado Silva;Anthony Bagnall (2024) Data Mining and Knowledge Discovery.
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aeon: a Python toolkit for learning from time series
Matthew Middlehurst;Ali Ismail-Fawaz;Antoine Guillaume;Christopher Holder;David Guijo Rubio;Guzal Bulatova;Leonidas Tsaprounis;Lukasz Mentel;Martin Walter;Patrick Schäfer;Anthony Bagnall (2024) Journal of Machine Learning Research.
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The great multivariate time series classification bake off: a review and experimental evaluation of recent algorithmic advances
Alejandro Pasos Ruiz;Michael Flynn;James Large;Matthew Middlehurst;Anthony Bagnall (2021) Data Mining and Knowledge Discovery.
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HIVE-COTE 2.0: a new meta ensemble for time series classification
Matthew Middlehurst;James Large; Michael Flynn;Jason Lines;Aaron Bostrom;Anthony Bagnall (2021) Machine Learning.