Channouf Nabil, L'Ecuyer Pierre, Ingolfsson Armann, Avramidis Athanassios N
DIRO, Université de Montréal, Montréal, Canada.
Health Care Manag Sci. 2007 Feb;10(1):25-45. doi: 10.1007/s10729-006-9006-3.
We develop and evaluate time-series models of call volume to the emergency medical service of a major Canadian city. Our objective is to offer simple and effective models that could be used for realistic simulation of the system and for forecasting daily and hourly call volumes. Notable features of the analyzed time series are: a positive trend, daily, weekly, and yearly seasonal cycles, special-day effects, and positive autocorrelation. We estimate models of daily volumes via two approaches: (1) autoregressive models of data obtained after eliminating trend, seasonality, and special-day effects; and (2) doubly-seasonal ARIMA models with special-day effects. We compare the estimated models in terms of goodness-of-fit and forecasting accuracy. We also consider two possibilities for the hourly model: (3) a multinomial distribution for the vector of number of calls in each hour conditional on the total volume of calls during the day and (4) fitting a time series to the data at the hourly level. For our data, (1) and (3) are superior.
我们开发并评估了加拿大一个主要城市紧急医疗服务呼叫量的时间序列模型。我们的目标是提供简单有效的模型，可用于系统的实际模拟以及预测每日和每小时的呼叫量。所分析时间序列的显著特征包括：正趋势、日、周和年季节性周期、特殊日效应以及正自相关性。我们通过两种方法估计每日呼叫量模型：（1）对消除趋势、季节性和特殊日效应后获得的数据进行自回归模型；（2）具有特殊日效应的双季节性自回归整合移动平均模型。我们根据拟合优度和预测准确性对估计的模型进行比较。我们还考虑了每小时模型的两种可能性：（3）根据一天内呼叫总量，对每小时呼叫次数向量采用多项分布；（4）在小时级别对数据拟合时间序列。对于我们的数据，（1）和（3）更优。
