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Attention-Based SeriesNet: An Attention-Based Hybrid Neural Network Model for Conditional Time Series Forecasting

Department of Information Engineering, Graduate School of Engineering, Hiroshima University, Higashihiroshima 739-8527, Japan
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Information 2020, 11(6), 305; https://doi.org/10.3390/info11060305
Received: 4 May 2020 / Revised: 25 May 2020 / Accepted: 3 June 2020 / Published: 5 June 2020
Traditional time series forecasting techniques can not extract good enough sequence data features, and their accuracies are limited. The deep learning structure SeriesNet is an advanced method, which adopts hybrid neural networks, including dilated causal convolutional neural network (DC-CNN) and Long-short term memory recurrent neural network (LSTM-RNN), to learn multi-range and multi-level features from multi-conditional time series with higher accuracy. However, they didn’t consider the attention mechanisms to learn temporal features. Besides, the conditioning method for CNN and RNN is not specific, and the number of parameters in each layer is tremendous. This paper proposes the conditioning method for two types of neural networks, and respectively uses the gated recurrent unit network (GRU) and the dilated depthwise separable temporal convolutional networks (DDSTCNs) instead of LSTM and DC-CNN for reducing the parameters. Furthermore, this paper presents the lightweight RNN-based hidden state attention module (HSAM) combined with the proposed CNN-based convolutional block attention module (CBAM) for time series forecasting. Experimental results show our model is superior to other models from the viewpoint of forecasting accuracy and computation efficiency. View Full-Text
Keywords: attention; convolutional neural network; recurrent neural network attention; convolutional neural network; recurrent neural network
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Cheng, Y.; Liu, Z.; Morimoto, Y. Attention-Based SeriesNet: An Attention-Based Hybrid Neural Network Model for Conditional Time Series Forecasting. Information 2020, 11, 305.

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