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Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence pp 619–658Cite as

Deep Learning of Multisensory Streaming Data for Predictive Modelling with Applications in Finance, Ecology, Transport and Environment

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Part of the book series: Springer Series on Bio- and Neurosystems ((SSBN,volume 7))

Abstract

This chapter presents methods for using eSNN and BI-SNN for deep, incremental learning and predictive modelling of streaming data and for deep knowledge representation. The methods are applied for predictive modelling in the areas of finance, ecology, transport and environment using respective multisensory streaming data. Each of these applications require specific model design in terms of data preparation, SNN model parameters, experimental setting and validation. Each of the methods are illustrated with case study problems and data, but their applicability can be extended to a wider class of problems where multisensory streaming data is available.

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Acknowledgements

Parts of the material in this chapter have been previously published as referenced in the relevant sections of this chapter. I would like to acknowledge the contribution to these publications of my co-authors Enmei Tu, Josafath Israel Espinosa, Sue Worner, Reggio Hartono, Stefan Marks, Nathan Scott, S. Gulyaev, N. Sengupta, R. Khansam, V. Ravi, A. Gollahalli, Petr Maciak, Imanol Bilbao-Quintana.

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Authors and Affiliations

  1. Knowledge Engineering and Discovery Research Institute (KEDRI), Auckland University of Technology, Auckland, New Zealand

    Nikola K. Kasabov

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Correspondence to Nikola K. Kasabov .

Appendices

Appendix 1

Appendix 2

Improved stock market movement prediction with optimised eSNN parameters on the same stock data as in 19.2 (Fig. 19.24)

Fig. 19.24
figure 24

A grid optimisation of two eSNN parameters (number of receptive fields N and with of the receptive fields) of eSNN for BSE stock movement prediction. This resulted in a significant improvement of predicted stock value (max accuracy achieved is 90% for N = 11 and width = 1.6) (the figure is created by Imanol Bilbao-Quintana)

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Kasabov, N.K. (2019). Deep Learning of Multisensory Streaming Data for Predictive Modelling with Applications in Finance, Ecology, Transport and Environment. In: Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence . Springer Series on Bio- and Neurosystems, vol 7. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-57715-8_19

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