Low-Rank Temporal Attention-Augmented Bilinear Network for financial time-series forecasting
This work addresses efficiency issues for financial analysts dealing with ultra high-frequency data, but it is incremental as it builds on an existing model.
The authors tackled the challenge of forecasting financial time-series, specifically stock prices, by proposing a low-rank tensor approximation of the Temporal Attention-Augmented Bilinear network to reduce trainable parameters and increase speed.
Financial market analysis, especially the prediction of movements of stock prices, is a challenging problem. The nature of financial time-series data, being non-stationary and nonlinear, is the main cause of these challenges. Deep learning models have led to significant performance improvements in many problems coming from different domains, including prediction problems of financial time-series data. Although the prediction performance is the main goal of such models, dealing with ultra high-frequency data sets restrictions in terms of the number of model parameters and its inference speed. The Temporal Attention-Augmented Bilinear network was recently proposed as an efficient and high-performing model for Limit Order Book time-series forecasting. In this paper, we propose a low-rank tensor approximation of the model to further reduce the number of trainable parameters and increase its speed.