The Great Time Series Classification Bake Off: An Experimental Evaluation of Recently Proposed Algorithms. Extended Version. - Anthony J. Bagnall; Aaron Bostrom; James Large; Jason Lines (CoRR, 2016)

Simulated Data Experiments for Time Series Classification Part 1: Accuracy Comparison with Default Settings. - Anthony J. Bagnall; Aaron Bostrom; James Large; Jason Lines (CoRR, 2017)

The Heterogeneous Ensembles of Standard Classification Algorithms (HESCA): the Whole is Greater than the Sum of its Parts. - James Large; Jason Lines; Anthony J. Bagnall (CoRR, 2017)

The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances. - Anthony J. Bagnall; Jason Lines; Aaron Bostrom; James Large; Eamonn J. Keogh (Data Min. Knowl. Discov., 2017)

Detecting Forged Alcohol Non-invasively Through Vibrational Spectroscopy and Machine Learning. - James Large; E. Kate Kemsley; Nikolaus Wellner; Ian Goodall; Anthony J. Bagnall (PAKDD (1), 2018)

Is rotation forest the best classifier for problems with continuous features? - Anthony J. Bagnall; Aaron Bostrom; Gavin C. Cawley; Michael Flynn; James Large; Jason Lines (CoRR, 2018)

From BOP to BOSS and Beyond: Time Series Classification with Dictionary Based Classifiers. - James Large; Anthony J. Bagnall; Simon Malinowski; Romain Tavenard (CoRR, 2018)

The UEA multivariate time series classification archive, 2018. - Anthony J. Bagnall; Hoang Anh Dau; Jason Lines; Michael Flynn; James Large; Aaron Bostrom; Paul Southam; Eamonn J. Keogh (CoRR, 2018)

The Contract Random Interval Spectral Ensemble (c-RISE): The Effect of Contracting a Classifier on Accuracy. - Michael Flynn; James Large; Tony Bagnall (HAIS, 2019)

Can Automated Smoothing Significantly Improve Benchmark Time Series Classification Algorithms? - James Large; Paul Southam; Anthony J. Bagnall (HAIS, 2019)

Mixing Hetero- and Homogeneous Models in Weighted Ensembles. - James Large; Anthony J. Bagnall (IDEAL (1), 2019)

A tale of two toolkits, report the second: bake off redux. Chapter 1. dictionary based classifiers. - Anthony J. Bagnall; James Large; Matthew Middlehurst (CoRR, 2019)

A probabilistic classifier ensemble weighting scheme based on cross-validated accuracy estimates. - James Large; Jason Lines; Anthony J. Bagnall (Data Min. Knowl. Discov., 2019)

On time series classification with dictionary-based classifiers. - James Large; Anthony J. Bagnall; Simon Malinowski; Romain Tavenard (Intell. Data Anal., 2019)

The Canonical Interval Forest (CIF) Classifier for Time Series Classification. - Matthew Middlehurst; James Large; Anthony J. Bagnall (IEEE BigData, 2020)

On the Usage and Performance of the Hierarchical Vote Collective of Transformation-Based Ensembles Version 1.0 (HIVE-COTE v1.0). - Anthony J. Bagnall; Michael Flynn; James Large; Jason Lines; Matthew Middlehurst (AALTD@PKDD/ECML, 2020)

The Temporal Dictionary Ensemble (TDE) Classifier for Time Series Classification. - Matthew Middlehurst; James Large; Gavin C. Cawley; Anthony J. Bagnall (ECML/PKDD (1), 2020)

A tale of two toolkits, report the third: on the usage and performance of HIVE-COTE v1.0. - Anthony J. Bagnall; Michael Flynn; James Large; Jason Lines; Matthew Middlehurst (CoRR, 2020)

Detecting Electric Devices in 3D Images of Bags. - Anthony J. Bagnall; Paul Southam; James Large; Richard W. Harvey (CoRR, 2020)

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 J. Bagnall (Data Min. Knowl. Discov., 2021)

HIVE-COTE 2.0: a new meta ensemble for time series classification. - Matthew Middlehurst; James Large; Michael Flynn; Jason Lines; Aaron Bostrom; Anthony J. Bagnall (Mach. Learn., 2021)