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Found 9 papers, 2 papers with code
HIVE-COTE 2.0: a new meta ensemble for time series classification
Since it was first proposed in 2016, the algorithm has remained state of the art for accuracy on the UCR time series classification archive.
A tale of two toolkits, report the third: on the usage and performance of HIVE-COTE v1.0
The Hierarchical Vote Collective of Transformation-based Ensembles (HIVE-COTE) is a heterogeneous meta ensemble for time series classification.
sktime: A Unified Interface for Machine Learning with Time Series
We present sktime -- a new scikit-learn compatible Python library with a unified interface for machine learning with time series.
The UEA multivariate time series classification archive, 2018
In 2002, the UCR time series classification archive was first released with sixteen datasets.
The Heterogeneous Ensembles of Standard Classification Algorithms (HESCA): the Whole is Greater than the Sum of its Parts
We show that the Heterogeneous Ensembles of Standard Classification Algorithms (HESCA), which ensembles based on error estimates formed on the train data, is significantly better (in terms of error, balanced error, negative log likelihood and area under the ROC curve) than its individual components, picking the component that is best on train data, and a support vector machine tuned over 1089 different parameter configurations.
Simulated Data Experiments for Time Series Classification Part 1: Accuracy Comparison with Default Settings
We describe what results we expected from each class of algorithm and data representation, then observe whether these prior beliefs are supported by the experimental evidence.
The Great Time Series Classification Bake Off: An Experimental Evaluation of Recently Proposed Algorithms. Extended Version
These algorithms have been evaluated on subsets of the 47 data sets in the University of California, Riverside time series classification archive.
Finding Motif Sets in Time Series
Two are greedy algorithms based on pairwise comparison, and the third uses a heuristic measure of set quality to find the motif set directly.
An Experimental Evaluation of Nearest Neighbour Time Series Classification
Specifically, we compare 1-NN classifiers with Euclidean and DTW distance to standard classifiers, examine whether the performance of 1-NN Euclidean approaches that of 1-NN DTW as the number of cases increases, assess whether there is any benefit of setting $k$ for $k$-NN through cross validation whether it is worth setting the warping path for DTW through cross validation and finally is it better to use a window or weighting for DTW.
