Dynamic sparse network; Time series classification; Data mining; Convolutional neural network; Receptive field
Abstract :
[en] 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.
Disciplines :
Computer science
Author, co-author :
Xiao, Qiao ✱; Eindhoven University of Technology, Netherlands
