Hydra: competing convolutional kernels for fast and accurate time series classification
Angus Dempster, Daniel F. Schmidt, Geoffrey I. Webb
Abstract
Abstract We demonstrate a simple connection between dictionary methods for time series classification, which involve extracting and counting symbolic patterns in time series, and methods based on transforming input time series using convolutional kernels, namely Rocket and its variants. We show that by adjusting a single hyperparameter it is possible to move by degrees between models resembling dictionary methods and models resembling Rocket . We present Hydra , a simple, fast, and accurate dictionary method for time series classification using competing convolutional kernels, combining key aspects of both Rocket and conventional dictionary methods. Hydra is faster and more accurate than the most accurate existing dictionary methods, achieving similar accuracy to several of the most accurate current methods for time series classification. Hydra can also be combined with Rocket and its variants to significantly improve the accuracy of these methods.