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Found 11 papers, 4 papers with code
Unsupervised Feature Based Algorithms for Time Series Extrinsic Regression
FreshPRINCE is a pipeline estimator consisting of a transform into a wide range of summary features followed by a rotation forest regressor.
Bake off redux: a review and experimental evaluation of recent time series classification algorithms
We introduce 30 classification datasets either recently donated to the archive or reformatted to the TSC format, and use these to further evaluate the best performing algorithm from each category.
A Review and Evaluation of Elastic Distance Functions for Time Series Clustering
Our conclusion is to recommend MSM with k-medoids as the benchmark algorithm for clustering time series with elastic distance measures.
The FreshPRINCE: A Simple Transformation Based Pipeline Time Series Classifier
There have recently been significant advances in the accuracy of algorithms proposed for time series classification (TSC).
The Temporal Dictionary Ensemble (TDE) Classifier for Time Series Classification
We demonstrate that the temporal dictionary ensemble (TDE) is more accurate than other dictionary based approaches.
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.
The Canonical Interval Forest (CIF) Classifier for Time Series Classification
We propose combining TSF and catch22 to form a new classifier, the Canonical Interval Forest (CIF).
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.
A tale of two toolkits, report the second: bake off redux. Chapter 1. dictionary based classifiers
We call this type of approach to TSC dictionary based classification.
A tale of two toolkits, report the first: benchmarking time series classification algorithms for correctness and efficiency
We demonstrate correctness through equivalence of accuracy on a range of standard test problems and compare the build time of the different implementations.
Scalable Dictionary Classifiers for Time Series Classification
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.
