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.

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.

Prediction of telephone calls load using Echo State Network with exogenous variables / Bianchi, FILIPPO MARIA; Scardapane, Simone; Uncini, Aurelio; Rizzi, Antonello; Sadeghian, Alireza. - In: NEURAL NETWORKS. - ISSN 0893-6080. - STAMPA. - 71:(2015), pp. 204-213. [10.1016/j.neunet.2015.08.010]

Prediction of telephone calls load using Echo State Network with exogenous variables

BIANCHI, FILIPPO MARIA;SCARDAPANE, SIMONE;UNCINI, Aurelio;RIZZI, Antonello;
2015

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.
2015
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.
Time-series; forecasting; prediction; echo state network; genetic algorithm; exogenous variables; call data records
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Prediction of telephone calls load using Echo State Network with exogenous variables / Bianchi, FILIPPO MARIA; Scardapane, Simone; Uncini, Aurelio; Rizzi, Antonello; Sadeghian, Alireza. - In: NEURAL NETWORKS. - ISSN 0893-6080. - STAMPA. - 71:(2015), pp. 204-213. [10.1016/j.neunet.2015.08.010]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/793940
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