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Visualising deep network time-series representations

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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.

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Code availability

Publicly available at https://github.com/CptPirx/Time-Viz.

Notes

  1. https://github.com/jsilter/parametric_tsne.

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Acknowledgements

Alexandros Iosifidis acknowledges funding from the Project DISPA (Grant 9041-00004B) funded by the Independent Research Fund Denmark.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Aarhus University, Aarhus, Denmark

    Błażej Leporowski & Alexandros Iosifidis

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  1. Błażej Leporowski
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  2. Alexandros Iosifidis
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Correspondence to Błażej Leporowski.

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Leporowski, B., Iosifidis, A. Visualising deep network time-series representations. Neural Comput & Applic 33, 16489–16498 (2021). https://doi.org/10.1007/s00521-021-06244-8

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