有向动态网络中基于模体演化的链路预测方法

以往传统的链路预测方法大多数针对无向网络,而实际上大多数社交网络是有向的,并且没有考虑网络中同一节点对之间的重复边以及微观演化信息,因此不能较好地解决有向动态网络中的链路预测问题。针对有向网络,将节点对之间的重复边信息转换为该节点对之间连边的权值;接着采用了基于三元组模体的演化模型,对滑动窗口中相邻时间片的模体转换概率进行统计后,采用指数加权滑动平均法对其进行时序分析得到不同模体转换概率的预测矩阵,进而使用该矩阵对网络中的链边进行预测。这不仅充分利用了网络微观演化信息,而且解决了动态网络中重复边的问题。最后对实验结果进行分析发现,在高全局聚类系数高平均度的网络中AUC相比Triad Transition Matrix方法提高了近0.01,而相比Common Neighbor方法提高更多。因此,所提方法能够较好地应用网络微观演化信息进行链路预测。     

ON GENERALIZED FRACTIONAL PROCESSES

Abstract. A class of stationary long-memory processes is proposed which is an extension of the fractional autoregressive moving-average (FARMA) model. The FARMA model is limited by the fact that it does not allow data with persistent cyclic (or seasonal) behavior to be considered. Our extension, which includes the FARMA model as a special case, makes use of the properties of the generating function of the Gegenbauer polynomials, and we refer to these models as Gegenbauer autoregressive moving-average (GARMA) models. While the FARMA model has a peak in the spectrum at f = 0, the GARMA process can model long-term periodic behavior for any frequency 0 f 0.5. Properties of the GARMA process are examined and techniques for generation of realizations, model identification and parameter estimation are proposed. The use of the GARMA model is illustrated through simulated examples as well as with classical sunspot data.     

2016年长江第1号洪水预报及调度影响分析

通过分析2016长江第1号洪水的水雨情发展、洪水组成、水情预报、调度还原计算成果等,解析了该场洪水的暴雨洪水特性、预报对调度的支撑作用以及三峡水库调度对城陵矶河段水位的影响。分析表明：金沙江、乌江来水对第1号洪水起筑底作用,三峡区间洪水则为该场洪水造峰,三者最大1d洪量占三峡入库来水比率分别达26.1%,15.6%,38.1%;第1号洪水期间,水情预报为调度决策提供了长预见期、较高精度的前提支撑,78,54,30,6 h预见期的三峡入库洪峰预报误差分别仅为-20.0%,-10.0%,-4.0%,0;三峡水库在第1号洪水期间通过防洪调度将入库洪峰流量削峰38%,最大拦蓄洪量约29亿m3,削减莲花塘站洪峰水位0.39 m左右,避免了城陵矶河段出现超保证水位。     

潮汐双向流条件下丁坝水流特性研究

以长江口南支河段为原型,概化并建立水槽数学模型,研究径潮双向流条件下丁坝附近的水流结构。研究表明:丁坝一侧河岸,丁坝下游潮差远大于上游,在丁坝上游潮差小于对岸侧,下游潮差大于对岸侧;落潮时,丁坝回流区与壅水区长度大于涨潮,转流时刻,丁坝坝头附近流速相对较大;河床底部剪切应力变化在涨落急时刻最大,且落潮大于涨潮,转流时刻坝头及坝身迎水侧剪切应力增加明显。     

基于KPCA-PSO-SVM的径流预测研究

为提高径流预测模型的准确性与稳定性,对KPCA-PSO-SVM的径流预测方法进行了研究。在分析径流影响因素的基础上,利用核主成分分析(KPCA)法对径流影响因子进行非线性特征提取,获得主成分作为支持向量机(SVM)的输入变量,建立了径流预测SVM模型,其中模型参数通过粒子群算法(PSO)进行优化。模型建立后,以新疆伊犁河雅马渡站中长期径流预测为例进行分析。预测分析结果表明,在拟合和检验阶段模型的平均相对误差分别为0.77%和7.64%,与其他预测模型比较,基于KPCA-PSO-SVM方法建立的径流预测模型有较好的预测和泛化能力,是一种行之有效的中长期径流预测方法。     

昭通横江流域年径流预测

针对昭通横江由南到北贯穿昭通全境,水资源量丰富,但南北差异大,南部少、北部多的特点,根据流域内水文站实测年径流、大气环流指数、海温指数及其他指数等资料系列,采用多元回归分析法拟合1981—2010年径流,建立了预测模型,验证、预测分析了2011—2016年径流量。结果表明:干流控制站预测精度高于干流上段及支流代表站,水利工程建设等人类活动影响是导致出现这样结果的主要原因;多元回归分析在横江流域径流预测中具有一定的适应性,但因指标的差异及相关参数可获取性的差异,预测结果存在区域间、等级间的差别。     

城市径流总量控制指标分解及实现路径探索与实践

针对海绵城市径流总量控制目标,提出一种在城市规划体系中控制性详细规划和修建性详细规划阶段分解落实年径流总量控制率的方法。在控制性详细规划阶段分解径流总量控制指标时,分步骤先后确定各项低影响开发措施的面积率和下沉深度;在修建性详细规划阶段,配合径流总量控制指标分解结果,给出适宜的各项低影响开发措施的技术实施导则,以保证各单项指标的正确实施,并以某项目为例介绍了技术实施导则的构建方法。     

A DISTANCE MEASURE FOR CLASSIFYING ARIMA MODELS

Abstract. In a number of practical problems where clustering or choosing from a set of dynamic structures is needed, the introduction of a distance between the data is an early step in the application of multivariate statistical methods. In this paper a parametric approach is proposed in order to introduce a well-defined metric on the class of autoregressive integrated moving-average (ARIMA) invertible models as the Euclidean distance between their autoregressive expansions. Two case studies for clustering economic time series and for assessing the consistency of seasonal adjustment procedures are discussed. Finally, some related proposals are surveyed and some suggestions for further research are made.     

动态贝叶斯最小二乘支持向量机

为了对涡扇发动机的运行参数变化进行实时高精度预测,提出一种基于动态贝叶斯最小二乘支持向量机(LS-SVM)的时间序列预测算法。该算法将贝叶斯证据框架理论用于推断LS-SVM的初始模型参数;然后,利用样本增减迭代学习算法实现LS-SVM的参数动态调整。对某型涡扇发动机的摩擦力矩时间序列进行动态预测,并与动态LS-SVM模型的预测结果进行比较。结果显示,动态贝叶斯LS-SVM具有较好的预测精度。     

THE ESTIMATION AND PREDICTION OF SUBSET BILINEAR TIME SERIES MODELS WITH APPLICATIONS

Abstract. In this paper we define subset bilinear time series models, and then describe an algorithm for the estimation of these models. It is also pointed out that for this class of non-linear time series models, it is possible to obtain optimal several step predictors. The estimation technique of these models is illustrated with respect to three time series, and the optimal several steps ahead forecasts of these time series models are calculated. A comparison of these forecasts is made with the forecasts obtained by the best linear autoregressive and threshold autoregressive models. The residuals obtained from the models are tested for independence and Gaussianity using higher order moments.