# Data Normalization for Bilinear Structures in High-Frequency Financial Time-series

arXiv: https://arxiv.org/abs/2003.00598 · HTML: https://mlsanity.com/paper/2003.00598/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Mar 2020.

## Built on by (most cited first, 16 of 16)

- [Training-efficient density quantum machine learning](https://mlsanity.com/paper/2405.20237/) (2024)
- [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)
- [HLOB -- Information Persistence and Structure in Limit Order Books](https://mlsanity.com/paper/2405.18938/) (2024)
- [Deep Limit Order Book Forecasting](https://mlsanity.com/paper/2403.09267/) (2024)
- [TLOB: A Novel Transformer Model with Dual Attention for Price Trend Prediction with Limit Order Book Data](https://mlsanity.com/paper/2502.15757/) (2025)
- [Bilinear Input Normalization for Neural Networks in Financial Forecasting](https://mlsanity.com/paper/2109.00983/) (2021)
- [Applications of Reinforcement Learning in Finance -- Trading with a Double Deep Q-Network](https://mlsanity.com/paper/2206.14267/) (2022)
- [Extended Deep Adaptive Input Normalization for Preprocessing Time Series Data for Neural Networks](https://mlsanity.com/paper/2310.14720/) (2023)
- [Bridging Distribution Gaps in Time Series Foundation Model Pretraining with Prototype-Guided Normalization](https://mlsanity.com/paper/2504.10900/) (2025)
- [Quantile Online Learning for Semiconductor Failure Analysis](https://mlsanity.com/paper/2303.07062/) (2023)
- [Leveraging Deep Learning and Online Source Sentiment for Financial Portfolio Management](https://mlsanity.com/paper/2309.16679/) (2023)
- [Knowledge Distillation By Sparse Representation Matching](https://mlsanity.com/paper/2103.17012/) (2021)
- [Cryptocurrency Portfolio Optimization by Neural Networks](https://mlsanity.com/paper/2310.01148/) (2023)
- [TABL-ABM: A Hybrid Framework for Synthetic LOB Generation](https://mlsanity.com/paper/2510.22685/) (2025)
- [Inference-Time Decision Calibration for Temporal Classification](https://mlsanity.com/paper/2606.16034/) (2026)

## Abstract (arXiv)

Financial time-series analysis and forecasting have been extensively studied over the past decades, yet still remain as a very challenging research topic. Since the financial market is inherently noisy and stochastic, a majority of financial time-series of interests are non-stationary, and often obtained from different modalities. This property presents great challenges and can significantly affect the performance of the subsequent analysis/forecasting steps. Recently, the Temporal Attention augmented Bilinear Layer (TABL) has shown great performances in tackling financial forecasting problems. In this paper, by taking into account the nature of bilinear projections in TABL networks, we propose Bilinear Normalization (BiN), a simple, yet efficient normalization layer to be incorporated into TABL networks to tackle potential problems posed by non-stationarity and multimodalities in the input series. Our experiments using a large scale Limit Order Book (LOB) consisting of more than 4 million order events show that BiN-TABL outperforms TABL networks using other state-of-the-arts normalization schemes by a large margin.