Spiking Neural Networks for Predictive and Explainable Modelling of Multimodal Streaming Data with a Case Study on Financial Time Series and Online News

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aut.relation.articlenumber18367
aut.relation.issue1
aut.relation.journalScientific Reports
aut.relation.startpage18367
aut.relation.volume13
dc.contributor.authorAbouHassan, I
dc.contributor.authorKasabov, NK
dc.contributor.authorJagtap, V
dc.contributor.authorKulkarni, P
dc.date.accessioned2023-11-08T23:01:59Z
dc.date.available2023-11-08T23:01:59Z
dc.date.issued2023-10-26
dc.description.abstractIn 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.
dc.identifier.citationScientific Reports, ISSN: 2045-2322 (Print); 2045-2322 (Online), Springer Science and Business Media LLC, 13(1), 18367-. doi: 10.1038/s41598-023-42605-0
dc.identifier.doi10.1038/s41598-023-42605-0
dc.identifier.issn2045-2322
dc.identifier.issn2045-2322
dc.identifier.urihttp://hdl.handle.net/10292/16878
dc.languageeng
dc.publisherSpringer Science and Business Media LLC
dc.relation.urihttps://www.nature.com/articles/s41598-023-42605-0
dc.rightsOpen Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
dc.rights.accessrightsOpenAccess
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subject46 Information and Computing Sciences
dc.subject4611 Machine Learning
dc.subject7 Affordable and Clean Energy
dc.titleSpiking Neural Networks for Predictive and Explainable Modelling of Multimodal Streaming Data with a Case Study on Financial Time Series and Online News
dc.typeJournal Article
pubs.elements-id528630
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