We have proposed an ensemble scheme for TSC based on constructing classifiers on different data representations. The standard baseline algorithms used in TSC research are 1-NN with Euclidean distance and/or Dynamic Time Warping. We have conclusively shown that COTE significantly out-performs both of these approaches, and that COTE it is significantly better than all of the competing algorithms that have been proposed in the literature. We believe the results we present represents a new state-of-the-art in TSC that new algorithms should be compared to in terms of accuracy.
|Number of pages||2|
|Publication status||Published - May 2016|
|Event||32nd International Conference on Data Engineering (ICDE) - Helsinki, Finland|
Duration: 16 May 2016 → 20 May 2016
|Conference||32nd International Conference on Data Engineering (ICDE)|
|Period||16/05/16 → 20/05/16|