Why Use Evolving Neuro-Fuzzy and Spiking Neural Networks for incremental and explainable learning of time series? A case study on predictive modelling of trade imports and outlier detection

Abstract

What algorithms to choose for an incremental, predictive, and more importantly, explainable learning and modelling of time series data, and specifically – economic and financial time series? Will these algorithms reveal and explain abnormality in time series over time? This problem is part of the challenges in the area of explainable AI and life-long learning systems. Three widely used evolving connectionist systems (ECOS) that offer a solution to the above problems, are compared in the paper. The first two models, EFuNN and DENFIS, are neuro-fuzzy models that deal with vector-based data, while the spiking neural network model NeuCube deals with temporal and spatio-temporal data. The case study data used to demonstrate the qualities of these techniques is time series data related to Bulgarian petroleum oil imports from various trading partners. Graphical visualization enhances the deep analysis, pattern recognition, and knowledge extraction from the models. Conclusions are drawn in the sense that each of these techniques reveals different aspects of the data and the problem in hand and a single model solution of a complex problem is never going to be complete. Future work outlines integration of time series and on-line news to achieve a better predictive accuracy and a better understanding of the data.