English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

There are now a broad range of time series classification (TSC) algorithmsdesigned to exploit different representations of the data. These have beenevaluated on a range of problems hosted at the UCR-UEA TSC Archive(www.timeseriesclassification.com), and there have been extensive comparativestudies. However, our understanding of why one algorithm outperforms another isstill anecdotal at best. This series of experiments is meant to help provideinsights into what sort of discriminatory features in the data lead one set ofalgorithms that exploit a particular representation to be better than otheralgorithms. We categorise five different feature spaces exploited by TSCalgorithms then design data simulators to generate randomised data from eachrepresentation. We describe what results we expected from each class ofalgorithm and data representation, then observe whether these prior beliefs aresupported by the experimental evidence. We provide an open sourceimplementation of all the simulators to allow for the controlled testing ofhypotheses relating to classifier performance on different datarepresentations. We identify many surprising results that confounded ourexpectations, and use these results to highlight how an over simplified view ofclassifier structure can often lead to erroneous prior beliefs. We believeensembling can often overcome prior bias, and our results support the belief byshowing that the ensemble approach adopted by the Hierarchical Collective ofTransform based Ensembles (HIVE-COTE) is significantly better than thealternatives when the data representation is unknown, and is significantlybetter than, or not significantly significantly better than, or notsignificantly worse than, the best other approach on three out of five of theindividual simulators.

Simulated Data Experiments for Time Series Classification Part 1: Accuracy Comparison with Default Settings