DeepAI AI Chat
Log In Sign Up

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

07/23/2022
by   Mostafa Shabani, et al.
12

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.

READ FULL TEXT
01/14/2022

Multi-head Temporal Attention-Augmented Bilinear Network for Financial time series prediction

Financial time-series forecasting is one of the most challenging domains...
09/12/2018

Deep Co-investment Network Learning for Financial Assets

Most recent works model the market structure of the stock market as a co...
05/11/2020

Incremental Learning for End-to-End Automatic Speech Recognition

We propose an incremental learning for end-to-end Automatic Speech Recog...
04/17/2020

A Time Series Analysis-Based Stock Price Prediction Using Machine Learning and Deep Learning Models

Prediction of future movement of stock prices has always been a challeng...
04/22/2022

Causal Analysis of Generic Time Series Data Applied for Market Prediction

We explore the applicability of the causal analysis based on temporally ...
06/17/2022

Accelerating Machine Learning Training Time for Limit Order Book Prediction

Financial firms are interested in simulation to discover whether a given...