title: Prediction of telephone calls load using Echo State Network with exogenous variables.
abstract: We approach the problem of forecasting the load of incoming calls in a cell of a mobile network using Echo State Networks. With respect to previous approaches to the problem, we consider the inclusion of additional telephone records regarding the activity registered in the cell as exogenous variables, by investigating their usefulness in the forecasting task. Additionally, we analyze different methodologies for training the readout of the network, including two novel variants, namely ν-SVR and an elastic net penalty. Finally, we employ a genetic algorithm for both the tasks of tuning the parameters of the system and for selecting the optimal subset of most informative additional time-series to be considered as external inputs in the forecasting problem. We compare the performances with standard prediction models and we evaluate the results according to the specific properties of the considered time-series.
authors.affiliation: Department of Information Engineering, Electronics and Telecommunications (DIET), "Sapienza" University of Rome, Via Eudossiana 18, 00184 Rome, Italy. Electronic address: filippomaria.bianchi@uniroma1.it.
authors.forename: Filippo Maria
authors.initials: FM
authors.lastname: Bianchi
authors.affiliation: Department of Information Engineering, Electronics and Telecommunications (DIET), "Sapienza" University of Rome, Via Eudossiana 18, 00184 Rome, Italy. Electronic address: simone.scardapane@uniroma1.it.
authors.forename: Simone
authors.initials: S
authors.lastname: Scardapane
authors.affiliation: Department of Information Engineering, Electronics and Telecommunications (DIET), "Sapienza" University of Rome, Via Eudossiana 18, 00184 Rome, Italy. Electronic address: aurelio.uncini@uniroma1.it.
authors.forename: Aurelio
authors.initials: A
authors.lastname: Uncini
authors.affiliation: Department of Information Engineering, Electronics and Telecommunications (DIET), "Sapienza" University of Rome, Via Eudossiana 18, 00184 Rome, Italy. Electronic address: antonello.rizzi@uniroma1.it.
authors.forename: Antonello
authors.initials: A
authors.lastname: Rizzi
authors.affiliation: Department of Computer Science, Ryerson University, 350 Victoria Street, Toronto, ON M5B 2K3, Canada. Electronic address: asadeghi@ryerson.ca.
authors.forename: Alireza
authors.initials: A
authors.lastname: Sadeghian
mesh_terms: D000465:Algorithms; D040421:Cell Phone / Q000706:statistics & numerical data*; D003195:Computer Communication Networks; D005544:Forecasting; D000069550:Machine Learning; D008962:Models, Theoretical; D016571:Neural Networks, Computer
keywords: Call data records; Echo State Networks; Exogenous variables; Forecasting; Genetic algorithm; Time-series