A computational intelligence-based forecasting system for telecommunications time series

In this work a computational intelligence-based approach is proposed for forecasting outgoing telephone calls in a University Campus. A modified Takagi–Sugeno–Kang fuzzy neural system is presented, where the consequent parts of the fuzzy rules are neural networks with an internal recurrence, thus introducing the dynamics to the overall system. The proposed model, entitled Locally Recurrent Neurofuzzy Forecasting System (LR-NFFS), is compared to well-established forecasting models, where its particular characteristics are highlighted.