Time Series Data Classification Based on Dual Path CNN-RNN Cascade Network

Time series data classification is a significant topic as its application can be found in a various domain. Recent studies have shown that data-driven approach based on deep learning is powerful for data mining tasks. A typical deep learning method, Artificial Neural Network (ANN), has been proven to be capable for match complicated functions thus leading to the popularity. Convolutional neural network (CNN) is a special kind of ANN that has been widely used in the area of image processing tasks as its ability for extracting spatial features. However, it remains a challenge for implementing CNN in time series data classification. Recurrent Neural Network (RNN) is popular for tackling time series data as it can effectively utilize temporal information. But it is time-consuming to train RNN. This paper proposes a Dual Path CNN-RNN Cascade Network (DPCRCN) that achieves an end-to-end learning for classification. We use a dual path CNN to achieve a multi-size receptive field for better feature extraction, then using RNN and the following fully-connected layers to learn the map between the given features and the output. We also use Region of Interest (RoI) pooling to make our model capable for a flexible shape of data. We evaluate our model on Activity Recognition system based on Multisensor data fusion (AReM) dataset and we compare with many popular algorithms. We also evaluate our model using different shape of data. The results show that our model outperforms the alternatives. In addition, we provide the details of training our model.