THE RECURSIVE FITTING OF SUBSET VARX MODELS

Abstract. A vector time series model of the form A(L)y(t) + B(L)x(t) =ε(t) is known as a vector autoregressive model with exogenous variables (VARX model) and involves a regressand vector y(t) and a regressor vector x(t). This paper provides a method for the recursive fitting of subset VARX models. It suggests the use of ascending recursions in conjunction with an order selection criterion to choose an 'optimum' subset VARX model.     

报纸媒介系列广告形成特点与设计方式分析

无时间、地点限制反复刺激是报纸媒介系列广告的主要特点。因此,报纸媒介系列性广告在形成方式上具有其特有的时间性与空间性特点;而在广告的表现方式上则通常运用同类并列型、层次递进型、悬念升级型三种系列性设计方式来表现广告主题。     

数据挖掘课程案例教学研究

数据挖掘是一门交叉性学科,是情报学专业的重要课程之一。它主要介绍数据挖掘的基本概念、原理、方法和技术,涉及多个学科和算法因而教学难度较大。由于数据挖掘学科交叉性强涉及的挖掘方法和相关算法多并繁杂,造成学生对数据挖掘的整体工作流程缺乏了解形成“不识庐山真面目只缘身在此山中”现象。文中以时间序列服装销售额预测挖掘项目为教学案例让学生首先掌握数据挖掘的标准流程,重点讲解用到的相关挖掘方法和算法及其在实际挖掘环境中的开发方法,达到“会当I临绝顶一览众山小”的教学目标。通过教学实践教学效果良好。     

高压电气设备支架串联体系地震响应半解析法

通过论述高压电气设备支架串联体系地震响应半解析法的运算过程,说明地震响应半解析法对于电力系统防震工作的帮助。     

松材线虫病害高光谱时序与敏感特征研究

采用高光谱仪ASD FieldSpec Pro FR,连续观测了健康和发病马尾松针叶的时序高光谱,分析了松材线虫病害光谱的时序特征、敏感时期和敏感特征。结果表明:与健康马尾松相比,病害马尾松时序光谱差异较大;病害首先造成红边区域内光谱反射率减低,然后再出现红边蓝移的2阶段光谱变化规律;感染松材线虫的马尾松9月初已经出现了病态植被典型的光谱特征;近红外平台内最大的一阶微分值、红边内一阶微分的总和(SDr)与蓝边内一阶微分的总和(SDb)的比值等是指示病害发生的显著性高光谱特征。     

基于时空图卷积网络的多变量时间序列预测方法

为了扩大时空图卷积网络的预测范围,将它应用在关联关系未知场景下的多变量时间序列预测问题,提出一种附加图学习层的时空图卷积网络预测方法（GLB-STGCN）。图学习层借助余弦相似度从时间序列中学习图邻接矩阵,通过图卷积网络捕捉多变量之间的相互影响,最后通过多核时间卷积网络捕捉时间序列的周期性特征,实现对多变量的精准预测。为验证GLB-STGCN的有效性,使用天文、电力、交通和经济四个领域的公共数据集和一个工业场景生产数据集进行预测实验,结果表明GLB-STGCN优于对比方法,在天文数据集上的表现尤为出色,预测误差分别降低了6.02%、8.01%、6.72%和5.31%。实验结果证明GLB-STGCN适用范围更广,预测效果更好,尤其适合自然周期明显的时间序列预测问题。     

2010—2019年黄河干流兰州和白银段水质时空变化特征

基于综合污染指数法,结合单因子污染指数法及累积距平法等多种统计分析方法,定量分析了黄河流域兰州和白银段7个监测断面水质时空变化特征。结果表明:兰州段和白银段2010—2019年水质综合状况良好,各监测断面综合污染指数均在0.25以下;监测断面水质空间差异显著(P<0.05),兰州段整体水质优于白银段,扶和桥断面水质显著优于除新城桥外的其他断面(P<0.05);季节方面,兰州段综合污染指数在春、冬季高于夏、秋季(P<0.05),白银段水质季节性波动不明显;兰州段水质明显改善始于2014年,且经历了综合污染指数平稳下降(2014—2017年)和快速下降(2018—2019年)两个阶段;白银段水质发生转变时间滞后于兰州段(2016年),2017—2019年水质明显好转。     

空天地一体化遥感技术在安徽省2020年汛情监测中的应用

遥感技术能够快速反应、实时监控,成为汛情监测不可或缺的重要手段。安徽省内多大江大河,且降水集中在汛期,防汛工作压力大。2020年汛期,安徽省出现全省范围的特大汛情,淮河、长江、新安江三大流域接连告急。介绍了空天地一体化遥感技术在本次汛情监测中的应用:以国产高分系列卫星数据为主要数据源的航天遥感技术,以无人机拍摄为主要手段的航空遥感技术和以现场勘察为主的地面遥感技术一体化调度,为汛情监测提供了可靠的信息来源和有力的技术支撑。     

Hybrid function projective synchronization of chaotic systems with uncertain time-varying parameters via Fourier series expansion

In this paper, the hybrid function projective synchronization (HFPS) of different chaotic systems with uncertain periodically time-varying parameters is carried out by Fourier series expansion and adaptive bounding technique. Fourier series expansion is used to deal with uncertain periodically time-varying parameters. Adaptive bounding technique is used to compensate the bound of truncation errors. Using the Lyapunov stability theory, an adaptive control law and six parameter updating laws are constructed to make the states of two different chaotic systems asymptotically synchronized. The control strategy does not need to know the parameters thoroughly if the time-varying parameters are periodical functions. Finally, in order to verify the effectiveness of the proposed scheme, the HFPS between Lorenz system and Chen system is completed successfully by using this scheme.     

A new time invariant fuzzy time series forecasting method based on particle swarm optimization

In the analysis of time invariant fuzzy time series, fuzzy logic group relationships tables have been generally preferred for determination of fuzzy logic relationships. The reason of this is that it is not need to perform complex matrix operations when these tables are used. On the other hand, when fuzzy logic group relationships tables are exploited, membership values of fuzzy sets are ignored. Thus, in defiance of fuzzy set theory, fuzzy sets’ elements with the highest membership value are only considered. This situation causes information loss and decrease in the explanation power of the model. To deal with these problems, a novel time invariant fuzzy time series forecasting approach is proposed in this study. In the proposed method, membership values in the fuzzy relationship matrix are computed by using particle swarm optimization technique. The method suggested in this study is the first method proposed in the literature in which particle swarm optimization algorithm is used to determine fuzzy relations. In addition, in order to increase forecasting accuracy and make the proposed approach more systematic, the fuzzy c-means clustering method is used for fuzzification of time series in the proposed method. The proposed method is applied to well-known time series to show the forecasting performance of the method. These time series are also analyzed by using some other forecasting methods available in the literature. Then, the results obtained from the proposed method are compared to those produced by the other methods. It is observed that the proposed method gives the most accurate forecasts.