320 citations · 499 across the 6 of their papers we have counts for
6 papers
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…
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…
A Shapelet Transform for Multivariate Time Series Classification
Aaron Bostrom, Anthony Bagnall
Shapelets are phase independent subsequences designed for time series classification. We propose three adaptations to the Shapelet Transform (ST) to capture multivariate features i…
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…