A recurrent neural network–based forecasting system for telecommunications call volume is proposed in this work. In particular, the forecaster is a Block–Diagonal Recurrent Neural Network with internal feedback. Model’s performance is evaluated by use of real–world telecommunications data, where an extensive comparative analysis with a series of existing forecasters is conducted, including both traditional models as well as neural and fuzzy approaches.
Mastorocostas, Paris; Hilas, Constantinos; Varsamis, Dimitris; and Dova, Stergiani
"A Recurrent Neural Network–based Forecasting System for Telecommunications Call Volume,"
Applied Mathematics & Information Sciences: Vol. 07:
5, Article 1.
Available at: https://digitalcommons.aaru.edu.jo/amis/vol07/iss5/1