Bianchi Filippo Maria, Scardapane Simone, Uncini Aurelio, Rizzi Antonello, Sadeghian Alireza
Department of Information Engineering, Electronics and Telecommunications (DIET), "Sapienza" University of Rome, Via Eudossiana 18, 00184 Rome, Italy.
Department of Computer Science, Ryerson University, 350 Victoria Street, Toronto, ON M5B 2K3, Canada.
Neural Netw. 2015 Nov;71:204-13. doi: 10.1016/j.neunet.2015.08.010. Epub 2015 Sep 7.
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.
我们使用回声状态网络来解决预测移动网络小区中来电负荷的问题。相对于之前解决该问题的方法，我们通过研究小区中注册活动的额外电话记录作为外生变量在预测任务中的有用性，考虑将其纳入。此外，我们分析了训练网络读出的不同方法，包括两种新颖的变体，即ν-支持向量回归（ν-SVR）和弹性网罚则。最后，我们采用遗传算法来调整系统参数以及选择最具信息的额外时间序列的最优子集，以便在预测问题中作为外部输入。我们将性能与标准预测模型进行比较，并根据所考虑时间序列的特定属性评估结果。
