关键词: 时间序列分类, 深度学习, 卷积神经网络, 循环神经网络, 双向门控循环单元

Abstract: Time series data have the characteristics of non-discreteness, correlation between data and large feature space dimensions. Most current classification methods require complex data processing or feature engineering, which don??t take into account different time scale features and the timing dependencies between sequence data. In this paper, a new end-to-end deep learning neural network model named BiGRU-FCN is proposed by combining convolutional neural network and bidirectional gated recurrent unit of recurrent neural network. It does not require complex preprocessing of data. A plurality of features can be obtained through different networks operations, such as the spatial characteristics of convolutional neural networks on time series information and the bidirectional time-dependent characteristics of bidirectional recurrent neural networks in sequence, which can be used to classify the univariate time series data. The model is tested and evaluated on a large number of benchmark datasets. The experimental results show that the proposed model has higher accuracy than the existing methods and has preferable ability of classification.

Key words: time series classification, deep learning, convolutional neural network, recurrent neural network, bidirectional gated recurrent unit