476 citations · 1.5k across the 14 of their papers we have counts for
14 papers
The Multiverse of Time Series Machine Learning: an Archive for Multivariate Time Series Classification
Matthew Middlehurst, Aiden Rushbrooke, Ali Ismail-Fawaz +6
Time series machine learning (TSML) is a growing research field that spans a wide range of tasks. The popularity of established tasks such as classification, clustering, and extrin…
aeon: a Python toolkit for learning from time series
Matthew Middlehurst, Ali Ismail-Fawaz, Antoine Guillaume +8
aeon is a unified Python 3 library for all machine learning tasks involving time series. The package contains modules for time series forecasting, classification, extrinsic regress…
Unsupervised Feature Based Algorithms for Time Series Extrinsic Regression
David Guijo-Rubio, Matthew Middlehurst, Guilherme Arcencio +2
Time Series Extrinsic Regression (TSER) involves using a set of training time series to form a predictive model of a continuous response variable that is not directly related to th…
Bake off redux: a review and experimental evaluation of recent time series classification algorithms
Matthew Middlehurst, Patrick Schäfer, Anthony Bagnall
In 2017, a research paper compared 18 Time Series Classification (TSC) algorithms on 85 datasets from the University of California, Riverside (UCR) archive. This study, commonly re…
A Review and Evaluation of Elastic Distance Functions for Time Series Clustering
Chris Holder, Matthew Middlehurst, Anthony Bagnall
Time series clustering is the act of grouping time series data without recourse to a label. Algorithms that cluster time series can be classified into two groups: those that employ…
The FreshPRINCE: A Simple Transformation Based Pipeline Time Series Classifier
Matthew Middlehurst, Anthony Bagnall
There have recently been significant advances in the accuracy of algorithms proposed for time series classification (TSC). However, a commonly asked question by real world practiti…
The Temporal Dictionary Ensemble (TDE) Classifier for Time Series Classification
Matthew Middlehurst, James Large, Gavin Cawley +1
Using bag of words representations of time series is a popular approach to time series classification. These algorithms involve approximating and discretising windows over a series…
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 Canonical Interval Forest (CIF) Classifier for Time Series Classification
Matthew Middlehurst, James Large, Anthony Bagnall
Time series classification (TSC) is home to a number of algorithm groups that utilise different kinds of discriminatory patterns. One of these groups describes classifiers that pre…
Benchmarking Multivariate Time Series Classification Algorithms
Alejandro Pasos Ruiz, Michael Flynn, Anthony Bagnall
Time Series Classification (TSC) involved building predictive models for a discrete target variable from ordered, real valued, attributes. Over recent years, a new set of TSC algor…
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,…
A tale of two toolkits, report the second: bake off redux. Chapter 1. dictionary based classifiers
Anthony Bagnall, James Large, Matthew Middlehurst
Time series classification (TSC) is the problem of learning labels from time dependent data. One class of algorithms is derived from a bag of words approach. A window is run along…
A tale of two toolkits, report the first: benchmarking time series classification algorithms for correctness and efficiency
Anthony Bagnall, Franz Király, Markus Löning +2
sktime is an open source, Python based, sklearn compatible toolkit for time series analysis developed by researchers at the University of East Anglia (UEA), University College Lond…
Scalable Dictionary Classifiers for Time Series Classification
Matthew Middlehurst, William Vickers, Anthony Bagnall
Dictionary based classifiers are a family of algorithms for time series classification (TSC), that focus on capturing the frequency of pattern occurrences in a time series. The ens…