320 citations · 854 across the 11 of their papers we have counts for
11 papers
On time series clustering with k-means
Christopher Holder, Anthony Bagnall, Jason Lines
There is a long history of research into time series clustering using distance-based partitional clustering. Many of the most popular algorithms adapt k-means (also known as Lloyd'…
HIVE-COTE 2.0: a new meta ensemble for time series classification
Matthew Middlehurst, James Large, Michael Flynn +3
The Hierarchical Vote Collective of Transformation-based Ensembles (HIVE-COTE) is a heterogeneous meta ensemble for time series classification. HIVE-COTE forms its ensemble from cl…
A tale of two toolkits, report the third: on the usage and performance of HIVE-COTE v1.0
Anthony Bagnall, Michael Flynn, James Large +2
The Hierarchical Vote Collective of Transformation-based Ensembles (HIVE-COTE) is a heterogeneous meta ensemble for time series classification. Since it was first proposed in 2016,…
sktime: A Unified Interface for Machine Learning with Time Series
Markus Löning, Anthony Bagnall, Sajaysurya Ganesh +3
We present sktime -- a new scikit-learn compatible Python library with a unified interface for machine learning with time series. Time series data gives rise to various distinct bu…
The UEA multivariate time series classification archive, 2018
Anthony Bagnall, Hoang Anh Dau, Jason Lines +5
In 2002, the UCR time series classification archive was first released with sixteen datasets. It gradually expanded, until 2015 when it increased in size from 45 datasets to 85 dat…
Is rotation forest the best classifier for problems with continuous features?
A. Bagnall, M. Flynn, J. Large +3
In short, our experiments suggest that yes, on average, rotation forest is better than the most common alternatives when all the attributes are real-valued. Rotation forest is a tr…