Mastorocostas, P. A.Hilas, C. S.Varsamis, D. N.Dova, S. C.2015-06-282024-09-272015-06-282024-09-272013http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6707102&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D6707102https://repository2024.ihu.gr/handle/123456789/1544The problem of telecommunications call volume forecasting is addressed to in this work. In particular, a foreacasting system is proposed, that is based on a dynamic fuzzy-neural model, where the consequent parts of the fuzzy rules are small Block-Diagonal Recurrent Neural Networks with internal feedback. The forecasting characteristics are highlighted and the prediction performance is evaluated by use of real-world telecommunications data. An extensive comparative analysis with a series of existing forecasters is conducting, including both traditional models as well as fuzzy and neurofuzzy approaches.6enAttribution-NonCommercial-NoDerivatives 4.0 Διεθνέςhttp://creativecommons.org/licenses/by-nc-nd/4.0/Άρθρο σε επιστημονικό συνέδριο10.1109/IJCNN.2013.6707102Computational modelingForecastingMarket researchNeuronsPredictive modelsRecurrent neural networksTelecommunications