Abstract
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.
Highlights
In big data analysis, time series forecasting is an essential branch developed in recent years
This paper proposed a deep learning neural network structure named attention-based SeriesNet, which desires to predict the future value of time series
The attention-based SeriesNet applies dilated depthwise separable temporal convolutional networks (DDSTCNs) and Gated recurrent unit network (GRU) instead of dilated causal convolutional neural networks (DC-convolutional neural networks (CNN)) and long-short term memory (LSTM) in SerieNet to accelerate the training. This model adopts convolutional block attention module (CBAM) attention on residual learning module and proposed hidden state attention module (HSAM) attention on GRU networks to better extract the potential features from the input time series
Summary
Time series forecasting is an essential branch developed in recent years. Deep learning is an advanced approach to overcome these problems. It depends on non-linear modules to learn the fully features from the input data. SeriesNet, which combined the dilated causal convolutional neural networks (DC-CNN) [2] and the long-short term memory (LSTM) [3]. They evaluated that their model has higher forecasting accuracy and greater stableness. LSTM and DC-CNN are widely applied to time series forecasting with excellent performance. Gated recurrent unit network (GRU) [4] and LSTM have a comparable performance on time series forecasting, but parameter quantity significantly reduced
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