A Computational Intelligence Approach for Forecasting 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 internal recurrence, thus introducing 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.
- 3.Makridakis, SG, Wheelwright SC, McGee, VE (1998) Forecasting: methods and applications. 3d ed., Wiley, New YorkGoogle Scholar
- 4.Holt CE (2004) Forecasting trends and seasonals by exponentially weighted moving averages ONR Memorandum 52, Carnegie Institute of Technology, Pittsburg, (1957). (Reprinted: Int J Forecasting, 20:5–10)Google Scholar
- 12.Riedmiller M, Braun H (1993) A direct adaptive method for faster backpropagation learning: the RPROP algorithm. In: proceeding IEEE International Joint Conference on Neural Networks, pp 586–591Google Scholar
- 13.Werbos PJ (1974) Beyond regression: New tools for prediction and analysis in the behavioral sciences, Ph.D. Thesis, Harvard University, CambridgeGoogle Scholar