武汉空气质量预测及受周边城市污染影响分析

利用武汉及其周边城市长沙、南昌、合肥、襄阳、孝感等城市空气质量指数(AQI)及相关污染物数据,探索了武汉市空气质量指数的统计分布规律及空气污染治理效果的评价问题,给出了能较好预测空气质量指数等级的推理规则.用统计模型分析武汉及周边大中城市的SO2等空气污染物之间的传播及相互影响.     

Hierarchical nonparametric survival modeling for demand forecasting with fragmented categorical covariates

This paper addresses the problem of data fragmentation when incorporating imbalanced categorical covariates in nonparametric survival models. The problem arises in an application of demand forecasting where certain categorical covariates are important explanatory factors for the diversity of survival patterns but are severely imbalanced in the sense that a large percentage of data segments defined by these covariates have very small sample sizes. Two general approaches, called the class‐based approach and the fusion‐based approach, are proposed to handle the problem. Both reply on judicious utilization of a data segment hierarchy defined by the covariates. The class‐based approach allows certain segments in the hierarchy to have their private survival functions and aggregates the others to share a common survival function. The fusion‐based approach allows all survival functions to borrow and share information from all segments based on their positions in the hierarchy. A nonparametric Bayesian estimator with Dirichlet process priors provides the data‐sharing mechanism in the fusion‐based approach. The hyperparameters in the priors are treated as fixed quantities and learned from data by taking advantage of the data segment hierarchy. The proposed methods are motivated and validated by a case study with real‐world data from an operation of software development service.     

滑坡时间预报的非平稳时间序列方法研究

本文基于边坡变形破坏的特点,对位移观测数据进行统计分析,利用非平稳时间序列理论就位移观测值建立模型进而作出预报。通过实例验证,该理论方法在边坡变形破坏时间预报中是有效可行的。     

成都市信息技术产业的计量经济模型

本文针对成都市信息技术产业的实际情况,依据市电子工业局提供的数据建立了成都市信息技术产业的计量经济模型．该模型由８ 个方程组成,包含８ 个内生变量和１２ 个外生变量．根据模型的方程体系,对成都市现有的电子信息产业结构进行分析,揭示了市电子工业总产出的影响因素,提出了该行业结构优化的建议方案,并给出了一个可行的预测方程;分部类排序结果表明成都市信息技术产业化仍停留在分立元器件基础阶段,产业发展水平亟待提高     

基于蚁群算法的灰色组合预测模型

分别利用灰色GM(1,1)模型、GM(1,1)优化模型和新息GM(1,1)模型建立三个单项预测模型,进一步建立了组合灰色预测模型,组合模型的权系数利用蚁群算法确定.最后给出了一个我国人口数量组合预测模型,计算结果表明,基于蚁群算法的灰色组合预测模型的拟合和预测精度要优于传统组合预测模型.     

基于AGA-SVM的非线性组合预测模型

为提高预测精度,提出基于AGA-SVM的非线性组合预测模型.以组合预测模型的误差平方和最小为优化准则,用加速遗传算法对支持向量机参数进行优化选择,并利用支持向量机对单一模型的预测结果进行组合预测.算例结果表明,AGASVM综合利用了各单个预测模型的重要预测信息,其预测误差远远小于各单个模型的预测误差,其预测精度更高,模型的实用性更强.     

Kalman滤波方法在湖南省居民收入预测中的应用

本文利用Kalman滤波方法,对湖南省历年农村和城镇居民收入进行了预测,并比较了农村和城镇居民收入情况,结果与实际值相当吻合.并比较了农村和城镇居民收入情况,该方法对于防止收入差距进一步扩大有非常重要的实际意义.     

修正的GM(1,1)残差模型在原煤销售量预测中的应用

本论研究的是提高灰色预测模型的一种方法。首先建立主模型进行预测，得到残差序列，然后对残差序列建模，对主模型进行修正，得到修正的GM(1，1)模型。将模型应用到原煤销售量的预测中，其精度明显提高。     

Coherent mortality forecasting with generalized linear models: A modified time-transformation approach

In this paper, we propose an alternative approach for forecasting mortality for multiple populations jointly. Our contribution is developed upon the generalized linear models introduced by Renshaw et al., (1996) and Sithole et al., (2000), in which mortality forecasts are generated within the model structure, without the need of additional stochastic processes. To ensure that the resulting forecasts are coherent, a modified time-transformation is developed to stipulate the expected mortality differential between two populations to remain constant when the long-run equilibrium is attained. The model is then further extended to incorporate a structural change, an important property that is observed in the historical mortality data of many national populations. The proposed modeling methods are illustrated with data from two different pairs of populations: (1) Swedish and Danish males; (2) English and Welsh males and U.K. male insured lives.     

基于粒子群-支持向量机定量降水集合预报方法

首先对ECMWF不同物理量场预报因子群进行自然正交展开,选取能充分反映每个预报因子场主要信息的第一主分量作为模型输入.进一步利用粒子群算法对支持向量回归机的相关参数进行优化,以南宁市8个气象站单站逐日降水作为预报对象,建立粒子群-支持向量回归集合预报模型,进行单站逐日降水的数值预报产品释用预报方法研究.利用模型对2015年5-6月南宁市8站进行了逐日降水预报业务试验,结果表明,模型具有较好的预报效果.并提出了利用隶属函数建立可信度函数对不同的预报模型进行评价.