# Low-Rank Temporal Attention-Augmented Bilinear Network for financial time-series forecasting

arXiv: https://arxiv.org/abs/2107.06995 · HTML: https://mlsanity.com/paper/2107.06995/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 33 references that are in the map.
- First posted on arXiv 5 Jul 2021.

## Builds on (most cited first, 8 of 8)

- [Tensor Decomposition for Signal Processing and Machine Learning](https://mlsanity.com/paper/1607.01668/) (2016)
- [Benchmark Dataset for Mid-Price Forecasting of Limit Order Book Data with Machine Learning Methods](https://mlsanity.com/paper/1705.03233/) (2017)
- [Temporal Attention augmented Bilinear Network for Financial Time-Series Data Analysis](https://mlsanity.com/paper/1712.00975/) (2017)
- [Using Deep Learning for price prediction by exploiting stationary limit order book features](https://mlsanity.com/paper/1810.09965/) (2018)
- [Temporal Logistic Neural Bag-of-Features for Financial Time series Forecasting leveraging Limit Order Book Data](https://mlsanity.com/paper/1901.08280/) (2019)
- [Data-driven Neural Architecture Learning For Financial Time-series Forecasting](https://mlsanity.com/paper/1903.06751/) (2019)
- [Feature Engineering for Mid-Price Prediction with Deep Learning](https://mlsanity.com/paper/1904.05384/) (2019)
- [Mid-price Prediction Based on Machine Learning Methods with Technical and Quantitative Indicators](https://mlsanity.com/paper/1907.09452/) (2019)

## Built on by (most cited first, 2 of 2)

- [Multi-head Temporal Attention-Augmented Bilinear Network for Financial time series prediction](https://mlsanity.com/paper/2201.05459/) (2022)
- [Predicting the State of Synchronization of Financial Time Series using Cross Recurrence Plots](https://mlsanity.com/paper/2210.14605/) (2022)

## Abstract (arXiv)

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.