  The receptive field (RF), which determines the region of time series to be
``seen'' and used, is critical to improve the performance for time series
classification (TSC). However, the variation of signal scales across and within
time series data, makes it challenging to decide on proper RF sizes for TSC. In
this paper, we propose a dynamic sparse network (DSN) with sparse connections
for TSC, which can learn to cover various RF without cumbersome
hyper-parameters tuning. The kernels in each sparse layer are sparse and can be
explored under the constraint regions by dynamic sparse training, which makes
it possible to reduce the resource cost. The experimental results show that the
proposed DSN model can achieve state-of-art performance on both univariate and
multivariate TSC datasets with less than 50\% computational cost compared with
recent baseline methods, opening the path towards more accurate resource-aware
methods for time series analyses. Our code is publicly available at:
https://github.com/QiaoXiao7282/DSN.
