  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, and can be combined with ROCKET and
its variants to further improve the accuracy of these methods.
