  In recent years, two competitive time series classification models, namely,
ROCKET and MINIROCKET, have garnered considerable attention due to their low
training cost and high accuracy. However, they rely on a large number of random
1-D convolutional kernels to comprehensively capture features, which is
incompatible with resource-constrained devices. Despite the development of
heuristic algorithms designed to recognize and prune redundant kernels, the
inherent time-consuming nature of evolutionary algorithms hinders efficient
evaluation. To efficiently prune models, this paper eliminates feature groups
contributing minimally to the classifier, thereby discarding the associated
random kernels without direct evaluation. To this end, we incorporate both
group-level ($l_{2,1}$-norm) and element-level ($l_2$-norm) regularizations to
the classifier, formulating the pruning challenge as a group elastic net
classification problem. An ADMM-based algorithm is initially introduced to
solve the problem, but it is computationally intensive. Building on the
ADMM-based algorithm, we then propose our core algorithm, POCKET, which
significantly speeds up the process by dividing the task into two sequential
stages. In Stage 1, POCKET utilizes dynamically varying penalties to
efficiently achieve group sparsity within the classifier, removing features
associated with zero weights and their corresponding kernels. In Stage 2, the
remaining kernels and features are used to refit a $l_2$-regularized classifier
for enhanced performance. Experimental results on diverse time series datasets
show that POCKET prunes up to 60% of kernels without a significant reduction in
accuracy and performs 11$\times$ faster than its counterparts. Our code is
publicly available at https://github.com/ShaowuChen/POCKET.
