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In the last five years there have been a large number of new time seriesclassification algorithms proposed in the literature. These algorithms havebeen evaluated on subsets of the 47 data sets in the University of California,Riverside time series classification archive. The archive has recently beenexpanded to 85 data sets, over half of which have been donated by researchersat the University of East Anglia. Aspects of previous evaluations have madecomparisons between algorithms difficult. For example, several differentprogramming languages have been used, experiments involved a single train/testsplit and some used normalised data whilst others did not. The relaunch of thearchive provides a timely opportunity to thoroughly evaluate algorithms on alarger number of datasets. We have implemented 18 recently proposed algorithmsin a common Java framework and compared them against two standard benchmarkclassifiers (and each other) by performing 100 resampling experiments on eachof the 85 datasets. We use these results to test several hypotheses relating towhether the algorithms are significantly more accurate than the benchmarks andeach other. Our results indicate that only 9 of these algorithms aresignificantly more accurate than both benchmarks and that one classifier, theCollective of Transformation Ensembles, is significantly more accurate than allof the others. All of our experiments and results are reproducible: we releaseall of our code, results and experimental details and we hope these experimentsform the basis for more rigorous testing of new algorithms in the future.

The Great Time Series Classification Bake Off: An Experimental Evaluation of Recently Proposed Algorithms. Extended Version