# Temporal Attention augmented Bilinear Network for Financial Time-Series Data Analysis

arXiv: https://arxiv.org/abs/1712.00975 · HTML: https://mlsanity.com/paper/1712.00975/

## Key facts

- Cited by 46 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Dec 2017.

## Built on by (most cited first, 30 of 46)

- [A General Survey on Attention Mechanisms in Deep Learning](https://mlsanity.com/paper/2203.14263/) (2022)
- [Self-supervised Autoregressive Domain Adaptation for Time Series Data](https://mlsanity.com/paper/2111.14834/) (2021)
- [Bayesian Learning for Neural Networks: an algorithmic survey](https://mlsanity.com/paper/2211.11865/) (2022)
- [A Survey of Explainable Artificial Intelligence (XAI) in Financial Time Series Forecasting](https://mlsanity.com/paper/2407.15909/) (2024)
- [Multi-period Learning for Financial Time Series Forecasting](https://mlsanity.com/paper/2511.08622/) (2025)
- [Multi-Horizon Forecasting for Limit Order Books: Novel Deep Learning Approaches and Hardware Acceleration using Intelligent Processing Units](https://mlsanity.com/paper/2105.10430/) (2021)
- [LOB-Based Deep Learning Models for Stock Price Trend Prediction: A Benchmark Study](https://mlsanity.com/paper/2308.01915/) (2023)
- [Multi-head Temporal Attention-Augmented Bilinear Network for Financial time series prediction](https://mlsanity.com/paper/2201.05459/) (2022)
- [Long Short-Term Memory Neural Network for Financial Time Series](https://mlsanity.com/paper/2201.08218/) (2022)
- [HLOB -- Information Persistence and Structure in Limit Order Books](https://mlsanity.com/paper/2405.18938/) (2024)
- [Distributional Drift Adaptation with Temporal Conditional Variational Autoencoder for Multivariate Time Series Forecasting](https://mlsanity.com/paper/2209.00654/) (2022)
- [Deep Limit Order Book Forecasting](https://mlsanity.com/paper/2403.09267/) (2024)
- [Tensor-Train Recurrent Neural Networks for Interpretable Multi-Way Financial Forecasting](https://mlsanity.com/paper/2105.04983/) (2021)
- [How Robust are Limit Order Book Representations under Data Perturbation?](https://mlsanity.com/paper/2110.04752/) (2021)
- [AURSAD: Universal Robot Screwdriving Anomaly Detection Dataset](https://mlsanity.com/paper/2102.01409/) (2021)
- [Fighting Money Laundering with Statistics and Machine Learning](https://mlsanity.com/paper/2201.04207/) (2022)
- [Bayesian Bilinear Neural Network for Predicting the Mid-price Dynamics in Limit-Order Book Markets](https://mlsanity.com/paper/2203.03613/) (2022)
- [Augmented Bilinear Network for Incremental Multi-Stock Time-Series Classification](https://mlsanity.com/paper/2207.11577/) (2022)
- [Axial-LOB: High-Frequency Trading with Axial Attention](https://mlsanity.com/paper/2212.01807/) (2022)
- [TLOB: A Novel Transformer Model with Dual Attention for Price Trend Prediction with Limit Order Book Data](https://mlsanity.com/paper/2502.15757/) (2025)
- [Quasi Black-Box Variational Inference with Natural Gradients for Bayesian Learning](https://mlsanity.com/paper/2205.11568/) (2022)
- [Explaining the Unexplainable: A Systematic Review of Explainable AI in Finance](https://mlsanity.com/paper/2503.05966/) (2025)
- [Detecting Faults during Automatic Screwdriving: A Dataset and Use Case of Anomaly Detection for Automatic Screwdriving](https://mlsanity.com/paper/2107.01955/) (2021)
- [Low-Rank Temporal Attention-Augmented Bilinear Network for financial time-series forecasting](https://mlsanity.com/paper/2107.06995/) (2021)
- [Bilinear Input Normalization for Neural Networks in Financial Forecasting](https://mlsanity.com/paper/2109.00983/) (2021)
- [Optimum Output Long Short-Term Memory Cell for High-Frequency Trading Forecasting](https://mlsanity.com/paper/2304.09840/) (2023)
- [Copula Variational LSTM for High-dimensional Cross-market Multivariate Dependence Modeling](https://mlsanity.com/paper/2305.08778/) (2023)
- [Time-Series Foundation AI Model for Value-at-Risk Forecasting](https://mlsanity.com/paper/2410.11773/) (2024)
- [LOBERT: Generative AI Foundation Model for Limit Order Book Messages](https://mlsanity.com/paper/2511.12563/) (2025)
- [Manifold Gaussian Variational Bayes on the Precision Matrix](https://mlsanity.com/paper/2210.14598/) (2022)

## Abstract (arXiv)

Financial time-series forecasting has long been a challenging problem because of the inherently noisy and stochastic nature of the market. In the High-Frequency Trading (HFT), forecasting for trading purposes is even a more challenging task since an automated inference system is required to be both accurate and fast. In this paper, we propose a neural network layer architecture that incorporates the idea of bilinear projection as well as an attention mechanism that enables the layer to detect and focus on crucial temporal information. The resulting network is highly interpretable, given its ability to highlight the importance and contribution of each temporal instance, thus allowing further analysis on the time instances of interest. Our experiments in a large-scale Limit Order Book (LOB) dataset show that a two-hidden-layer network utilizing our proposed layer outperforms by a large margin all existing state-of-the-art results coming from much deeper architectures while requiring far fewer computations.