Spiking neural networks for predictive and explainable modelling of multimodal streaming data with a case study on financial time series and online news

Iman AbouHassan, Nikola K. Kasabov, Vinayak Jagtap, Parag Kulkarni

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)
13 Downloads (Pure)

Abstract

In a first study, this paper argues and demonstrates that spiking neural networks (SNN) can be successfully used for predictive and explainable modelling of multimodal streaming data. The paper proposes a new method, where both time series and on-line news are integrated as numerical streaming data in the same time domain and then used to train incrementally a SNN model. The connectivity and the spiking activity of the SNN are then analyzed through clustering and dynamic graph extraction to reveal on-line interaction between all input variables in regard to the predicted one. The paper answers the main research question of how to understand the dynamic interaction of time series and on-line news through their integrative modelling. It offers a new method to evaluate the efficiency of using on-line news on the predictive modelling of time series. Results on financial stock time series and online news are presented. In contrast to traditional machine learning techniques, the method reveals the dynamic interaction between stock variables and news and their dynamic impact on model accuracy when compared to models that do not use news information. Along with the used financial data, the method is applicable to a wide range of other multimodal time series and news data, such as economic, medical, environmental and social. The proposed method, being based on SNN, promotes the use of massively parallel and low energy neuromorphic hardware for multivariate on-line data modelling.
Original languageEnglish
Article number18367
Pages (from-to)1-14
Number of pages14
JournalScientific Reports
Volume13
Issue number1
Early online date26 Oct 2023
DOIs
Publication statusPublished online - 26 Oct 2023

Bibliographical note

Funding Information:
IA has been partially funded for her PhD study fee from the Knowledgeegineering.ai. The other authors have been partially supported by their respective institutions. No project funding has been utilized.

Publisher Copyright:
© 2023, The Author(s).

Keywords

  • Computational science
  • Engineering
  • Information technology
  • Mathematics and computing

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