Forecasting of clustered time series with recurrent neural networks and a fuzzy clustering scheme

Abstract

Fuzzy c-neural network models (FCNNM) combine clustering techniques with advanced neural networks for time series modeling in order to make predictions for a possibly large set of time series using only a small number of models. Given a set of time series, FCNNM finds a partition matrix that quantifies to which degree each time series is associated with each prediction model, as well as the parameters of the neural network models for each cluster. FCNNM allows to automatically identify groups of time series with similar dynamics. This results in higher data efficiency, being of particular interest in cases of poor data availability. We illustrate the application of FCNNM to cash withdrawal series as part of an effective cash management.