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…
The Heterogeneous Ensembles of Standard Classification Algorithms (HESCA): the Whole is Greater than the Sum of its Parts
James Large, Jason Lines, Anthony Bagnall
Building classification models is an intrinsically practical exercise that requires many design decisions prior to deployment. We aim to provide some guidance in this decision maki…
Simulated Data Experiments for Time Series Classification Part 1: Accuracy Comparison with Default Settings
Anthony Bagnall, Aaron Bostrom, James Large +1
There are now a broad range of time series classification (TSC) algorithms designed to exploit different representations of the data. These have been evaluated on a range of proble…
The Great Time Series Classification Bake Off: An Experimental Evaluation of Recently Proposed Algorithms. Extended Version
Anthony Bagnall, Aaron Bostrom, James Large +1
In the last five years there have been a large number of new time series classification algorithms proposed in the literature. These algorithms have been evaluated on subsets of th…
Finding Motif Sets in Time Series
Anthony Bagnall, Jon Hills, Jason Lines
Time-series motifs are representative subsequences that occur frequently in a time series; a motif set is the set of subsequences deemed to be instances of a given motif. We focus…
An Experimental Evaluation of Nearest Neighbour Time Series Classification
Anthony Bagnall, Jason Lines
Data mining research into time series classification (TSC) has focussed on alternative distance measures for nearest neighbour classifiers. It is standard practice to use 1-NN with…