Visualising deep network time-series representations

Błażej Leporowski*, Alexandros Iosifidis

*Corresponding author for this work

    Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

    Abstract

    Despite the popularisation of machine learning models, more often than not, they still operate as black boxes with no insight into what is happening inside the model. There exist a few methods that allow to visualise and explain why a model has made a certain prediction. Those methods, however, allow visualisation of the link between the input and output of the model without presenting how the model learns to represent the data used to train the model as whole. In this paper, a method that addresses that issue is proposed, with a focus on visualising multi-dimensional time-series data. Experiments on a high-frequency stock market dataset show that the method provides fast and discernible visualisations. Large datasets can be visualised quickly and on one plot, which makes it easy for a user to compare the learned representations of the data. The developed method successfully combines known techniques to provide an insight into the inner workings of time-series classification models.

    Original languageEnglish
    JournalNeural Computing and Applications
    Volume33
    Issue23
    Pages (from-to)16489-16498
    Number of pages10
    ISSN0941-0643
    DOIs
    Publication statusPublished - Dec 2021

    Keywords

    • Financial data
    • Limit order book
    • Neural network representations visualisation
    • Time-series visualisation

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