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.
KeywordsFuzzy Rule Mean Absolute Percentage Error Recurrent Neural Network Exponential Smoothing Consequent Part
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