前向人工神经网络模型用于时间序列分析

介绍前向人工神经网络模型，并与传统的统计学线性模型进行比较。为了适应时间序列变量的特点，对逆传播模型进行了改进，并对太阳黑子活动的观察值这一典型的时间序列进行了分析，并检验了模型的预测能力，取得了较好的结果     

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.     

高维进化策略调整神经网络结构和参数

提出一种基于佳点集原理的进化策略用于神经网络结构和参数的调整.为了克服正交设计法的一些不足来处理高维最优化问题,本文采用分步交叉框架,将佳点集技术引入实数域交叉算子增强高维空间的搜索能力.前馈神经网络的隐含节点与连接边数从小逐步递增直至学习效果足够好.通过调整能得到一个部分连接的前馈网络,减少了网络实现的耗费.最后,佳点集进化策略有效应用于生成预测太阳黑子的演化神经网络.实验结果证明了新方法的有效性.     

改进的Kriging技术实时重构区域电离层foF2的分布

改进了kriging技术重构电离层F2层临界频率foF2的方法,利用国际参考电离层IRI-2001模型作为背景场,通过拟合国际参考电离层模型得到“等效太阳黑子数”,并利用kriging技术来完成区域电离层的重构,是区域电离层重构的一种方法,它适合重构区域内电离层探测站较少的情况。与直接利用实测临界频率进行kriging重构相比,改进的kriging技术提高了重构的精度和稳健性。也表明了背景电离层模型在电离层重构中的重要作用。     

近50年淮河流域旱涝与太阳黑子的关系研究

根据淮河流域1961～2010年的月降水资料,用Z指数法分析了淮河流域旱涝灾害规律,并采用频率分析法分析了太阳黑子极值年附近的旱涝特征、Morlet小波分析淮河流域的旱涝指数和太阳黑子数时间序列之间的周期性和相关性。结果表明,Z指数能较好地反映淮河流域旱涝灾害的实际情况,且洪涝化程度随淮河流域旱涝事件频率增加而加大;淮河流域旱涝灾害与太阳活动有一定的对应关系,淮河水系地区分别在太阳黑子m、M年附近易发生涝灾、旱灾,旱涝指数约存在4、10、16年的主要周期;沂沭泗水系地区在太阳黑子M年附近易出现旱灾,旱涝指数约存在3、12、30年的主要周期,但局部时间段上有较明显的相关性,随时间段的不同,相关变化也不同。     

Bayesian Subset Model Selection for Time Series

Abstract.  This paper considers the problem of subset model selection for time series. In general, a few lags which are not necessarily continuous, explain lag structure of a time-series model. Using the reversible jump Markov chain technique, the paper develops a fully Bayesian solution for the problem. The method is illustrated using the self-exciting threshold autoregressive (SETAR), bilinear and AR models. The Canadian lynx data, the Wolfe's sunspot numbers and Series A of Box and Jenkins (1976) are analysed in detail.     

A TEST FOR LINEARITY OF STATIONARY TIME SERIES

Abstract. A standard assumption that is often made in time series analysis is that the series conforms to a linear model. The object of this paper is to describe statistical tests for testing this assumption. The tests are constructed from the bispectral density function, and depend on the application of Hotelling T ². These tests are illustrated with two real time series and four simulated time series. Some guidelines about the choice of the parameters are also included.     

基于物理成因识别的第二松花江汛期径流预报

针对第二松花江流域中长期径流预测精度较低问题,为了分析物理预报因子的作用过程以提高汛期洪水预报精度,选取太阳黑子相对数为物理影响因素,进而识别其影响时滞,以影响时滞期内的太阳黑子相对数作为径流预报因子,以汛期(6—9月份)月平均径流为预报项目,采用BP神经网络识别映射关系,采用历史资料作为训练样本,完成网络训练和检验。以第二松花江干流控制性水利工程丰满水库为例,对2017年汛期月平均径流进行预报。结果表明:丰满水库汛期月平均入库流量为1 400 m~3/s,来水频率为11%,定性预报第二松花江流域2017年为丰水年;2017年丰满水库实际来水141.00亿m~3,为多年均值的112%,为偏丰来水年份,来水定性预报正确。该方法的创新点在于:采用全局敏感性分析方法识别太阳活动的影响时滞,以确定预报因子;采用BP网络模拟预报因子与预报项目的复杂非线性相关关系,以构建预报网络。研究成果为2017年吉林省水文预报和防汛决策提供了重要支撑。     

河南省旱涝灾害与太阳黑子活动的相关性分析

鉴于河南省旱涝灾害发生频繁,根据河南省1953～2006年的年平均降水量资料和1952～2005年的太阳黑子相对数月值数据,采用修正后的降水Z指数法划分了河南省旱涝等级,并分析了河南省旱涝灾害与太阳黑子活动之间的关系。结果表明,河南省旱灾易在太阳黑子活动的M年（峰值年）发生,而涝灾易发生在太阳黑子活动的m年（谷值年）及其前后一年。     

Outlier Detection And Estimation In NonLinear Time Series

Abstract. The problem of identifying the time location and estimating the amplitude of outliers in nonlinear time series is addressed. A model‐based method is proposed for detecting the presence of additive or innovational outliers when the series is generated by a general nonlinear model. We use this method for identifying and estimating outliers in bilinear, self‐exciting threshold autoregressive and exponential autoregressive models. A simulation study is performed to test the proposed procedures and comparing them with the methods based on linear models and linear interpolators. Finally, our results are applied for detecting outliers in the Canadian lynx trappings and in the sunspot numbers data.