Today's call center managers face multiple operational decision-making tasks. One of the most common is determining the weekly staffing levels to ensure customer satisfaction and meeting their needs while minimizing service costs. An initial step for producing the weekly schedule is forecasting the future system loads which involves predicting both arrival counts and average service times. We introduce an arrival count model which is based on a mixed Poisson process approach. The model is applied to data from an Israeli Telecom company call center. In our model, we also consider the effect of events such as billing on the arrival process and we demonstrate how to incorporate them as exogenous variables in the model. After obtaining the forecasted system load, in large call centers, a manager can choose to apply the QED (Quality-Efficiency Driven) regime's "square-root staffing" rule in order to balance the offered-load per server with the quality of service. Implementing this staffing rule requires that the forecasted values of the arrival counts and average service times maintain certain levels of precision. We develop different goodness of fit criteria that help determine our model's practical performance under the QED regime. These show that during most hours of the day the model can reach desired precision levels.
Forecasting the cost of drought events in France by Super Learning
在法国称为自然灾害赔偿计划的法律框架内，干旱事件是第二大最昂贵的自然灾害类型。近年来，干旱事件在地理范围和强度上都是显著的。我们开发和应用一种新的方法来预测法国干旱事件的成本。该方法取决于超级学习（van der Laan等人，2007; Benkeser等人，2018），一种通用聚合策略，用于通过依赖于算法库的自组织风险函数来学习所识别的数据的规律的特征。这些算法要么竞争（离散超级学习），要么合作（连续超级学习），交叉验证方案分别确定性能最佳的算法或算法组合。我们的超级学习器考虑到干旱事件的空间和时间性质在数据中引起的复杂依赖结构。
Winning the Big Data Technologies Horizon Prize: Fast and reliable forecasting of electricity grid traffic by identification of recurrent fluctuations
Biofisika Institute (CSIC, UPV/EHU)（生物物理研究所（西班牙国家研究委员会、巴斯克大学））；University of the Basque Country (UPV/EHU)（巴斯克大学）；IKERBASQUE, Basque Foundation for Science（巴斯克科学基金会（IKERBASQUE））
