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

Mostafa Shabani, Dat Thanh Tran, Martin Magris, Juho Kanniainen, Alexandros Iosifidis

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


    Financial time-series forecasting is one of the most challenging domains in the field of time-series analysis. This is mostly due to the highly non-stationary and noisy nature of financial time-series data. With progressive efforts of the community to design specialized neural networks incorporating prior domain knowledge, many financial analysis and forecasting problems have been successfully tackled. The temporal attention mechanism is a neural layer design that recently gained popularity due to its ability to focus on important temporal events. In this paper, we propose a neural layer based on the ideas of temporal attention and multi-head attention to extend the capability of the underlying neural network in focusing simultaneously on multiple temporal instances. The effectiveness of our approach is validated using large-scale limit-order book market data to forecast the direction of mid-price movements. Our experiments show that the use of multi-head temporal attention modules leads to enhanced prediction performances compared to baseline models.

    Original languageEnglish
    Title of host publication30th European Signal Processing Conference, EUSIPCO 2022 - Proceedings
    Number of pages5
    Publication dateOct 2022
    ISBN (Print)978-1-6654-6799-5
    ISBN (Electronic)978-90-827970-9-1
    Publication statusPublished - Oct 2022
    Event30th European Signal Processing Conference (EUSIPCO) - Belgrade, Serbia
    Duration: 29 Aug 20222 Sept 2022


    Conference30th European Signal Processing Conference (EUSIPCO)


    • Attention mechanism
    • Deep learning
    • Financial Time-series
    • Limit Order Book


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