Time series prediction with granular neural networks

Conventional artificial neural networks are inherently equipped with an ambiguous (uncertain) structure which is hard to be quantified and explained. Time series forecasting using neural networks thus becomes a highly challenging task also due to the fact that time series data are always nonlinear and uncertain (because of some disturbances). Considering this, we propose a granular neural network -based time series prediction model connecting the uncertainty of models and data with the concept of information granularity. We aim to provide an explainable time series prediction model to resist the dis-turbance inner time series data and reduce the vagueness of the model. The functionalities of the granular neural network model are threefold: (1) It reveals the uncertainty of a time series data set through the level of granularity, coverage and specificity and possesses high prediction accuracy; (2) It provides an opti-mized interval output endowed with enough specificity and sufficient coverage and this interval is more robust than a single value; (3) It owns an interpretable and flexible structure that reflects the uncertainty of the initial numeric neural network and the generalization and robustness of connections while being faced with disturbances. The experimental studies elaborate on each function of our model in detail and show that the developed method performed better than the existing approaches present in the literature when experimenting on several time series data sets.(c) 2023 Elsevier B.V. All rights reserved.