We introduce a new method for forecasting emergency call arrival rates that combines integer-valued time series models with a dynamic latent factor structure. Covariate information is captured via simple constraints on the factor loadings. We directly model the count-valued arrivals per hour, rather than using an artificial assumption of normality. This is crucial for the emergency medical service context, in which the volume of calls may be very low. Smoothing splines are used in estimating the factor levels and loadings to improve long-term forecasts. We impose time series structure at the hourly level, rather than at the daily level, capturing the fine-scale dependence in addition to the long-term structure. Our analysis considers all emergency priority calls received by Toronto EMS between January 2007 and December 2008 for which an ambulance was dispatched. Empirical results demonstrate significantly reduced error in forecasting call arrival volume. To quantify the impact of reduced forecast errors, we design a queueing model simulation that approximates the dynamics of an ambulance system. The results show better performance as the forecasting method improves. This notion of quantifying the operational impact of improved statistical procedures may be of independent interest.
High-dimensional point forecast combinations for emergency department demand
Lee Kong Chian School of Medicine, Nanyang Technological University（南洋理工大学李光前医学院）；Department of Economics, Rutgers University（罗格斯大学经济学系）；Saw Swee Hock School of Public Health, National University of Singapore（新加坡国立大学 Saw Swee Hock 公共卫生学院）；Ministry of Health, Singapore（新加坡卫生部）；Tan Tock Seng Hospital, Singapore（新加坡陈笃生医院）
A unified machine learning approach to time series forecasting applied to demand at emergency departments
MRC Centre for Global Infectious Disease Analysis, Imperial College London（帝国理工学院MRC全球传染病分析中心）；Imperial College Healthcare NHS Trust（帝国理工学院医疗保健国民健康服务体系信托）；Faculty of Medicine, Imperial College London（帝国理工学院医学院）；Department of Infectious Disease, Imperial College London（帝国理工学院传染病系）；Department of Mathematics, Imperial College London（帝国理工学院数学系）
XGBoost meets INLA: a two-stage spatio-temporal forecasting of wildfires in Portugal
University of Glasgow（格拉斯哥大学）；University of St Andrews（圣安德鲁斯大学）；King Abdullah University of Science and Technology（阿卜杜拉国王科技大学）；Universidade de Lisboa（里斯本大学）；NOVA School of Science and Technology（新理工科学和技术学院）
Forecasting mortality associated emergency department crowding
Faculty of Medicine and Health Technology, Tampere University（坦佩雷大学医学与健康技术学院）；Emergency Department, Tampere University Hospital（坦佩雷大学医院急诊科）；Kanta-Häme Central Hospital（坎塔-海梅中心医院）；Faculty of Information Technology and Communication Sciences, Tampere University（坦佩雷大学信息技术与传播科学学院）；Centre for Vascular Surgery and Interventional Radiology, Tampere University Hospital（坦佩雷大学医院血管外科与介入放射中心）
