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

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

SUBMITTER: AbouHassan I 

PROVIDER: S-EPMC10603166 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature