# Augmented Bilinear Network for Incremental Multi-Stock Time-Series Classification

arXiv: https://arxiv.org/abs/2207.11577 · HTML: https://mlsanity.com/paper/2207.11577/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 70 references that are in the map.
- First posted on arXiv 23 Jul 2022.

## Builds on (most cited first, 30 of 30)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [Learning Transferable Features with Deep Adaptation Networks](https://mlsanity.com/paper/1502.02791/) (2015)
- [Deep Visual Domain Adaptation: A Survey](https://mlsanity.com/paper/1802.03601/) (2018)
- [Exploiting Linear Structure Within Convolutional Networks for Efficient Evaluation](https://mlsanity.com/paper/1404.0736/) (2014)
- [Speeding up Convolutional Neural Networks with Low Rank Expansions](https://mlsanity.com/paper/1405.3866/) (2014)
- [Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019](https://mlsanity.com/paper/1911.13288/) (2019)
- [A Review of Single-Source Deep Unsupervised Visual Domain Adaptation](https://mlsanity.com/paper/2009.00155/) (2020)
- [Transfer learning for time series classification](https://mlsanity.com/paper/1811.01533/) (2018)
- [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)
- [Operational Neural Networks](https://mlsanity.com/paper/1902.11106/) (2019)
- [Using Deep Learning for price prediction by exploiting stationary limit order book features](https://mlsanity.com/paper/1810.09965/) (2018)
- [Incremental multi-domain learning with network latent tensor factorization](https://mlsanity.com/paper/1904.06345/) (2019)
- [Heterogeneous Multilayer Generalized Operational Perceptron](https://mlsanity.com/paper/1804.05093/) (2018)
- [Automatic Construction of Multi-layer Perceptron Network from Streaming Examples](https://mlsanity.com/paper/1910.03437/) (2019)
- [Tensor Representation in High-Frequency Financial Data for Price Change Prediction](https://mlsanity.com/paper/1709.01268/) (2017)
- [Temporal Logistic Neural Bag-of-Features for Financial Time series Forecasting leveraging Limit Order Book Data](https://mlsanity.com/paper/1901.08280/) (2019)
- [ATL: Autonomous Knowledge Transfer from Many Streaming Processes](https://mlsanity.com/paper/1910.03434/) (2019)
- [Progressive Spatio-Temporal Graph Convolutional Network for Skeleton-Based Human Action Recognition](https://mlsanity.com/paper/2011.05668/) (2020)
- [Improving Efficiency in Convolutional Neural Network with Multilinear Filters](https://mlsanity.com/paper/1709.09902/) (2017)
- [Supervised Domain Adaptation: A Graph Embedding Perspective and a Rectified Experimental Protocol](https://mlsanity.com/paper/2004.11262/) (2020)
- [Automatic Online Multi-Source Domain Adaptation](https://mlsanity.com/paper/2109.01996/) (2021)
- [Data-driven Neural Architecture Learning For Financial Time-series Forecasting](https://mlsanity.com/paper/1903.06751/) (2019)
- [Not all domains are equally complex: Adaptive Multi-Domain Learning](https://mlsanity.com/paper/2003.11504/) (2020)
- [How informative is the Order Book Beyond the Best Levels? Machine Learning Perspective](https://mlsanity.com/paper/2203.07922/) (2022)
- [QuantNet: Transferring Learning Across Systematic Trading Strategies](https://mlsanity.com/paper/2004.03445/) (2020)
- [Bilinear Input Normalization for Neural Networks in Financial Forecasting](https://mlsanity.com/paper/2109.00983/) (2021)
- [Support Spinor Machine](https://mlsanity.com/paper/1709.03943/) (2017)

## Built on by (most cited first, 4 of 4)

- [HLOB -- Information Persistence and Structure in Limit Order Books](https://mlsanity.com/paper/2405.18938/) (2024)
- [Predicting the State of Synchronization of Financial Time Series using Cross Recurrence Plots](https://mlsanity.com/paper/2210.14605/) (2022)
- [Multi-task Meta Label Correction for Time Series Prediction](https://mlsanity.com/paper/2303.08103/) (2023)
- [Convolutional and Deep Learning based techniques for Time Series Ordinal Classification](https://mlsanity.com/paper/2306.10084/) (2023)

## Abstract (arXiv)

Deep Learning models have become dominant in tackling financial time-series analysis problems, overturning conventional machine learning and statistical methods. Most often, a model trained for one market or security cannot be directly applied to another market or security due to differences inherent in the market conditions. In addition, as the market evolves through time, it is necessary to update the existing models or train new ones when new data is made available. This scenario, which is inherent in most financial forecasting applications, naturally raises the following research question: How to efficiently adapt a pre-trained model to a new set of data while retaining performance on the old data, especially when the old data is not accessible? In this paper, we propose a method to efficiently retain the knowledge available in a neural network pre-trained on a set of securities and adapt it to achieve high performance in new ones. In our method, the prior knowledge encoded in a pre-trained neural network is maintained by keeping existing connections fixed, and this knowledge is adjusted for the new securities by a set of augmented connections, which are optimized using the new data. The auxiliary connections are constrained to be of low rank. This not only allows us to rapidly optimize for the new task but also reduces the storage and run-time complexity during the deployment phase. The efficiency of our approach is empirically validated in the stock mid-price movement prediction problem using a large-scale limit order book dataset. Experimental results show that our approach enhances prediction performance as well as reduces the overall number of network parameters.