Attention on Attention Sparse Dense Convolutional Network for Financial Signal Processing

Abstract

Financial signal processing is a matter of great concern in FinTech. Traditionally, recurrent networks are often used to model time series, while the latest research shows that convolutional networks, especially temporal convolutional networks (TCNs), are also powerful and effective for a large number of sequence modeling tasks. The temporal convolutional network uses dilation convolution to expand the receptive field, resulting in very sparse connections in high network layers and no connection to neighbor points. Considered that short-term performance often has a more significant influence on the assets price movement, we suggest that TCNs are too sparse for financial signal processing. For a better solution, we propose a novel Attention on Attention Sparse Dense Convolutional Network (AoA-SDCN), which strengthens time decay characteristics by adding dense connections at close points. Moreover, we use the Attention on Attention mechanism to improve the performance further. Experimental results show that these techniques are effective for financial signal processing. The AoA-SDCN significantly outperforms state-of-the-art methods on Chinese commodity futures and stock datasets.