  Deep Neural Networks (DNN) have been successfully used to perform
classification and regression tasks, particularly in computer vision based
applications. Recently, owing to the widespread deployment of Internet of
Things (IoT), we identify that the classification tasks for time series data,
specifically from different sensors are of utmost importance. In this paper, we
present SRDCNN: Strongly Regularized Deep Convolution Neural Network (DCNN)
based deep architecture to perform time series classification tasks. The
novelty of the proposed approach is that the network weights are regularized by
both L1 and L2 norm penalties. Both of the regularization approaches jointly
address the practical issues of smaller number of training instances,
requirement of quicker training process, avoiding overfitting problem by
incorporating sparsification of weight vectors as well as through controlling
of weight values. We compare the proposed method (SRDCNN) with relevant
state-of-the-art algorithms including different DNNs using publicly available
time series classification benchmark (the UCR/UEA archive) time series datasets
and demonstrate that the proposed method provides superior performance. We feel
that SRDCNN warrants better generalization capability to the deep architecture
by profoundly controlling the network parameters to combat the training
instance insufficiency problem of real-life time series sensor signals.
