The impact of special days in call arrivals forecasting: A neural network approach to modelling special days

A key challenge for call centres remains the forecasting of high frequency call arrivals collected in hourly or shorter time buckets. In addition to the complex intraday, intraweek and intrayear seasonal cycles, call arrival data typically contain a large number of anomalous days, driven by the occurrence of holidays, special events, promotional activities and system failures. This study evaluates the use of a variety of univariate time series forecasting methods for forecasting intraday call arrivals in the presence of such outliers. Apart from established, statistical methods, we consider artificial neural networks (ANNs). Based on the modelling flexibility of the latter, we introduce and evaluate different methods to encode the outlying periods. Using intraday arrival series from a call centre operated by one of Europe’s leading entertainment companies, we provide new insights on the impact of outliers on the performance of established forecasting methods. Results show that ANNs forecast call centre data accurately, and are capable of modelling complex outliers using relatively simple outlier modelling approaches. We argue that the relative complexity of ANNs over standard statistical models is offset by the simplicity of coding multiple and unknown effects during outlying periods.     

关于三峡永久船闸高边坡快速施工地质超前预报的几个问题

文中就三峡永久船闸高边坡快速施工地质超前预报的几个问题进行了讨论。分别研究了三峡永久船闸高边坡施工地质超前预报的必要性，提出了超前预报技术思路，最后就高边坡岩体反分析及反馈设计问题、施工地质超前预报问题以及与监测相适应的超前处理及防护进行了分析。     

带常数项的最优加权组合预测

We propose a model based on the optimal weighted combinational forecasting with constant terms,give formulae of the weights and the average errors as well as a rela- tion of the model and the corresponding model without constant terms,and compare these models.Finally an example was given,which showed that the fitting precision has been enhanced.     

Forecasting daily supermarket sales using exponentially weighted quantile regression

Inventory control systems typically require the frequent updating of forecasts for many different products. In addition to point predictions, interval forecasts are needed to set appropriate levels of safety stock. The series considered in this paper are characterised by high volatility and skewness, which are both time-varying. These features motivate the consideration of forecasting methods that are robust with regard to distributional assumptions. The widespread use of exponential smoothing for point forecasting in inventory control motivates the development of the approach for interval forecasting. In this paper, we construct interval forecasts from quantile predictions generated using exponentially weighted quantile regression. The approach amounts to exponential smoothing of the cumulative distribution function, and can be viewed as an extension of generalised exponential smoothing to quantile forecasting. Empirical results are encouraging, with improvements over traditional methods being particularly apparent when the approach is used as the basis for robust point forecasting.     

A RAPID APPROXIMATION FOR PREDICTING THE HYDROCARBON DISCOVERY RATE: PART 2. IMPROVING THE ACCURACY

In Part 1 of this paper, we noted the systematic errors in the estimates of means and standard deviations produced by a rapid approximation applied to a model of hydrocarbon discovery. In Part 2, we apply regression to predict the approximation errors, as functions of model parameters and approximation output. With the regression model, we can correct much of the error in the approximation, as we illustrate with data from the Nisku-Shelf play of western Canada.     

中国现行财政体制下“买税”行为的博弈分析

博弈论为理论依据,通过对高、低两种任务下买税利益团体和上级监管部门的利益偏好和行为特征进行建模对比分析,考察影响其行为的各种因素,从经济学角度探求引起“买税”行为的根源,并试着给出建议.     

基于预测有效度的组合预测方法冗余信息的判定

基于预测方法有效度的概念 ,建立了考虑预测精度标准差的预测有效度的组合模型 ,提出了几个新的概念 ,给出冗余信息出现的两个判定定理 .并用实例说明判定定理的有效性 .     

基于集对论的居民消费研究

基于集对分析方法和近20年来的居民消费数据,建立了我国城镇居民的消费增量预测模型、进而研究了居民的消费水平。通过和常规的回归方法相比,阐明了集对分析方法在研究居民消费增量的等级变化问题方面的优点。研究结果也表明了集对分析方法在等级分类和趋势预测这一类似确定又不确定的问题上有良好的应用效果。     

用逐步二级分辨数学模型预测农业害虫种群动态

文章应用基本Ｆｉｓｈｅｒ准则下逐步二级分辨原理，对山东省临沂市１９６５～１９８６年（１９８０年除外）共２１年的第二代玉米螟虫株率的历史观测数据进行了数量分析，建立了３个逐步二级分辨数学模型。经对历史资料的回报验证，其历史符合率分别为９５．２４％、９２．３１％、１００％。将１９８７年、１９８８年观测数据作为独立样本进行试报，其预报结果与实际一致。     

灰色动态模型及其在人口预测中的应用

一个国家人口的数量直接影响着其经济、社会的发展和资源的利用 ,中国是世界上人口的第一大国 ,人口问题一直是制约中国发展的第一因素 .本文应用灰色系统等维灰数递补动态预测模型 ,对中国未来5 0年的人口数量进行了动态预测 ,通过检验表明 ,该模型合理、方法简便可行、结果符合实际 ,为中国经济和社会发展的决策和研究提供了科学依据