Integration of residual network and convolutional neural network along with various activation functions and global pooling for time series classification

In this paper, we devise a hybrid scheme, which integrates residual network with convolutional neural network, for time series classification. In the devised method, the architecture of network is constructed by facilitating a residual learning block at the first three convolutional layers to combine the strength of both methods. Further, different activation functions are used in different layers to achieve a decent abstraction. Additionally, to alleviate overfitting, the pooling operation is removed and the features are fed into a global average pooling instead of a fully connected layer. The resulting scheme requires no heavy preprocessing of raw data or feature crafting, thus could be easily deployed. To evaluate our method, we test it on 44 benchmark datasets and compare its performance with related methods. The results show that our method can deliver competitive performance among state-of-the-art methods. (C) 2019 Elsevier B.V. All rights reserved.