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View article: On time series clustering with k-means
On time series clustering with k-means Open
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's algorithm) to exploit time dependencies in the data by sp…
View article: Identifying earthquake swarms at Mt. Ruapehu, New Zealand: a machine learning approach
Identifying earthquake swarms at Mt. Ruapehu, New Zealand: a machine learning approach Open
Mt. Ruapehu is an active andesitic stratovolcano, consisting of several peaks with the summit plateau at 2,797 m, making it the tallest active volcano in New Zealand. The extent of the volcano spreads 40 km across with a series of complex …
View article: alan-turing-institute/sktime: v0.5.2
alan-turing-institute/sktime: v0.5.2 Open
What's New Fixed Fix ModuleNotFoundError issue (#613) @Hephaest Fixes _fit(X) in KNN (#610) @TonyBagnall UEA TSC module improvements 2 (#599) @TonyBagnall Fix sktime.classification.frequency_based not found error (#606) @Hephaest UEA TSC m…
View article: A tale of two toolkits, report the third: on the usage and performance of HIVE-COTE v1.0
A tale of two toolkits, report the third: on the usage and performance of HIVE-COTE v1.0 Open
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, the algorithm has undergone some minor changes and there i…
View article: sktime: A Unified Interface for Machine Learning with Time Series
sktime: A Unified Interface for Machine Learning with Time Series Open
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 but closely related learning tasks, such as forecasting and t…
View article: A probabilistic classifier ensemble weighting scheme based on cross-validated accuracy estimates
A probabilistic classifier ensemble weighting scheme based on cross-validated accuracy estimates Open
Our hypothesis is that building ensembles of small sets of strong classifiers constructed with different learning algorithms is, on average, the best approach to classification for real-world problems. We propose a simple mechanism for bui…
View article: The UEA multivariate time series classification archive, 2018
The UEA multivariate time series classification archive, 2018 Open
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 datasets. In October 2018 more datasets were added, bringing t…
View article: Is rotation forest the best classifier for problems with continuous features?
Is rotation forest the best classifier for problems with continuous features? Open
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 tree based ensemble that performs transforms on subsets of at…
View article: Time Series Classification with HIVE-COTE
Time Series Classification with HIVE-COTE Open
A recent experimental evaluation assessed 19 time series classification (TSC) algorithms and found that one was significantly more accurate than all others: the Flat Collective of Transformation-based Ensembles (Flat-COTE). Flat-COTE is an…
View article: The Heterogeneous Ensembles of Standard Classification Algorithms (HESCA): the Whole is Greater than the Sum of its Parts
The Heterogeneous Ensembles of Standard Classification Algorithms (HESCA): the Whole is Greater than the Sum of its Parts Open
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 making process. Specifically, given a classification problem wi…
View article: Simulated Data Experiments for Time Series Classification Part 1: Accuracy Comparison with Default Settings
Simulated Data Experiments for Time Series Classification Part 1: Accuracy Comparison with Default Settings Open
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 problems hosted at the UCR-UEA TSC Archive (www.timeseriesclassif…
View article: Simulated Data Experiments for Time Series Classification Part 1:\n Accuracy Comparison with Default Settings
Simulated Data Experiments for Time Series Classification Part 1:\n Accuracy Comparison with Default Settings Open
There are now a broad range of time series classification (TSC) algorithms\ndesigned to exploit different representations of the data. These have been\nevaluated on a range of problems hosted at the UCR-UEA TSC Archive\n(www.timeseriesclas…
View article: The Great Time Series Classification Bake Off: An Experimental\n Evaluation of Recently Proposed Algorithms. Extended Version
The Great Time Series Classification Bake Off: An Experimental\n Evaluation of Recently Proposed Algorithms. Extended Version Open
In the last five years there have been a large number of new time series\nclassification algorithms proposed in the literature. These algorithms have\nbeen evaluated on subsets of the 47 data sets in the University of California,\nRiversid…
View article: The Great Time Series Classification Bake Off: An Experimental Evaluation of Recently Proposed Algorithms. Extended Version
The Great Time Series Classification Bake Off: An Experimental Evaluation of Recently Proposed Algorithms. Extended Version Open
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 the 47 data sets in the University of California, Riverside t…