IaaS下基于预测的弹性云服务的研究

针对供应虚拟机的IaaS （infrastructure as a service）云下虚拟机部署存在较高时延,导致弹性云服务效率低下的问题,提出一种基于ARIMA模型和季节指数的动态负载预测及资源估算的方法. 该方法利用负载与虚拟机配置的关系,预测负载值,并估算虚拟机需求量,从而可提前部署虚拟机,提高IaaS云的服务效率. 研究结合供应虚拟机的私有IaaS云环境,实现其下的弹性供应的资源决策模块. 实验与算法分析表明,该方法能够准确决策虚拟机资源量,保证虚拟机资源预留,有效改善了IaaS云的弹性效率.     

Assessing time series models for forecasting international migration: Lessons from the United Kingdom

Migration is one of the most unpredictable demographic processes. The aim of this article is to provide a blueprint for assessing various possible forecasting approaches in order to help safeguard producers and users of official migration statistics against misguided forecasts. To achieve that, we first evaluate the various existing approaches to modelling and forecasting of international migration flows. Subsequently, we present an empirical comparison of ex post performance of various forecasting methods, applied to international migration to and from the United Kingdom. The overarching goal is to assess the uncertainty of forecasts produced by using different forecasting methods, both in terms of their errors (biases) and calibration of uncertainty. The empirical assessment, comparing the results of various forecasting models against past migration estimates, confirms the intuition about weak predictability of migration, but also highlights varying levels of forecast errors for different migration streams. There is no single forecasting approach that would be well suited for different flows. We therefore recommend adopting a tailored approach to forecasts, and applying a risk management framework to their results, taking into account the levels of uncertainty of the individual flows, as well as the differences in their potential societal impact.     

用于短时交通流预测的多项式分布滞后模型

提出了用于短时交通流预测的多项式分布滞后模型.其建模思想是交通状态时间序列同时受自身滞后项之外的多个因素影响,并且影响分布到了多个时段.通过与ARIMA模型(自回归整数移动平均模型)的预测精度对比分析,表明PDL（多项式分布滞后）模型具有与ARIMA相同的预测精度,而在模型可移植性、算法复杂性和实现方面更具优势.研究结果为短时交通流预测理论提供一种新的研究思路.     

r雪灾对湖北省GDP增速的影响

利用ARIMA模型,计算得出2008年湖北省第1季度GDP实际累计值比预测值要低一些,GDP累计增速有所减缓,认为这主要是2008年年初的雪灾所造成的,这场雪灾给很多行业造成了负面影响,对GDP增速有拉低作用．     

基于ARIMA风电机组齿轮箱故障趋势预测方法研究

针对齿轮箱计划外停机和意外故障导致的风电机组安全运行问题,提出了一种基于ARIMA模型的故障趋势预测方法;方法可以处理具有非线性和非平稳性特征的齿轮箱运行状态监测数据,用以时间序列的自相关分析为基础的模型预测状态监测时间序列数据的趋势变化;选择生产现场采集到的齿轮箱油泵出口压力SCADA数据和运行实例验证了方法的有效性,实验结果的拟合效果令人满意;研究结果表明方法能够适应齿轮箱运行状态监测数据随时间的变化特征,反映出一定的运行状态变化趋势,具有较好的预测精度和较大的应用范围,对风电机组其他部件的故障趋势预测具有一定的应用参考价值。     

A multiscale modeling approach incorporating ARIMA and anns for financial market volatility forecasting

The financial market volatility forecasting is regarded as a challenging task because of irreg ularity, high fluctuation, and noise. In this study, a multiscale ensemble forecasting model is proposed. The original financial series are decomposed firstly different scale components （i.e., approximation and details） using the maximum overlap discrete wavelet transform （MODWT）. The approximation is pre- dicted by a hybrid forecasting model that combines autoregressive integrated moving average （ARIMA） with feedforward neural network （FNN）. ARIMA model is used to generate a linear forecast, and then FNN is developed as a tool for nonlinear pattern recognition to correct the estimation error in ARIMA forecast. Moreover, details are predicted by Elman neural networks. Three weekly exchange rates data are collected to establish and validate the forecasting model. Empirical results demonstrate consistent better performance of the proposed approach.     

我国猪肉消费需求量集成预测——基于ARIMA、VAR和VEC模型的实证

猪肉消费需求量预测对稳定猪肉消费市场具有重要意义. 通过建立ARIMA、VAR和VEC模型, 利用Granger因果检验筛选出显著影响因素, 分别预测我国猪肉消费量. 最后, 基于动态集成预测方法对三种模型的预测结果进行综合集成. 通过对2009-2011年我国猪肉消费需求量预测, 实证结果表明样本外集成预测精度更高, 更稳定.     

GM-ARIMA模型在大坝安全监测中的应用

针对大坝安全监测的小样本数据既有一定趋势性又有一定波动性的特点,把灰色模型和时间序列模型结合起来运用在大坝安全监测中.首先利用灰色模型进行拟合和预测,然后对灰色残差序列建立ARIMA模型,对残差进行预测,最后将两者结合起来即可得到预测值.本文以小湾拱坝坝顶某测点的径向位移为例,建立GM-ARIMA进行拟合和预测,并与实测值比较.计算结果表明,与GM模型相比,GM-ARIMA模型的精度高,预测值更接近于实测值.     

Time-series forecasting using fractional differencing

The main failure of ARIMA modelling as used in practice are the limiting constraints imposed by differencing to achieve stationarity. The use of fractional differencing opens up a much wider and realistic behaviour for the trend and seasonal components than traditional integer differencing. This paper shows several advantages of using fractional differencing for forecasting monthly data. These advantages are illustrated using a sample of previously modelled time-series data selected from the literature.     

A semi‐parametric time series approach in modeling hourly electricity loads

In this paper we develop a semi‐parametric approach to model nonlinear relationships in serially correlated data. To illustrate the usefulness of this approach, we apply it to a set of hourly electricity load data. This approach takes into consideration the effect of temperature combined with those of time‐of‐day and type‐of‐day via nonparametric estimation. In addition, an ARIMA model is used to model the serial correlation in the data. An iterative backfitting algorithm is used to estimate the model. Post‐sample forecasting performance is evaluated and comparative results are presented. Copyright © 2006 John Wiley & Sons, Ltd.