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

Mostafa Shabani, Alexandros Iosifidis

    Research output: Contribution to book/anthology/report/proceedingArticle in proceedingsResearchpeer-review

    Abstract

    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.

    Original languageEnglish
    Title of host publication2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020
    Number of pages6
    PublisherIEEE
    Publication date1 Dec 2020
    Pages2156-2161
    Article number9308440
    ISBN (Electronic)9781728125473
    DOIs
    Publication statusPublished - 1 Dec 2020
    Event2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020 - Virtual, Canberra, Australia
    Duration: 1 Dec 20204 Dec 2020

    Conference

    Conference2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020
    Country/TerritoryAustralia
    CityVirtual, Canberra
    Period01/12/202004/12/2020
    SponsorIEEE Computational Intelligence Society
    SeriesProceedings (IEEE Symposium Series on Computational Intelligence)
    ISSN2472-8322

    Keywords

    • Deep learning
    • Financial time-series analysis
    • Limit Order Book data
    • Low-rank tensor decomposition

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