基于相空间重构的一阶加权局域预测法在溶解氧预测中的应用

将混沌理论引入到水质预测中,分析海河溶解氧时间序列,进行相空间重构和计算最大Lyapunov指数.结果表明,海河溶解氧时间序列具有混沌特性,可以对其进行短期预测.在此基础上,应用一阶加权局域预测模型进行了预测,预测结果具有一定的精度,为水质预测研究提供了新的思路.     

CPI数据预测计算的ANFIS模型结构探讨

依据CPI经济序列数据确定性混沌原理,探讨自适应神经模糊推理系统模型构造,并给出此类混沌数据列预测的ANFIS系统结构形式,进行CPI经济序列数据预测.并用实例拟合、预测数据证明:ANFIS模型是一种精度较高的混沌数据序列预报系统.为CPI数据预测提供了一种计算方法.     

基于Lyapunov指数的高炉铁水[Si]预报

通过对国内两座中型高炉冶炼过程的[S i]时间序列的混沌分析,计算出相应的Lyapunov指数谱.由最大Lyapunov指数为正,定量的说明了两座高炉冶炼过程具有混沌性,并估计了两座高炉冶炼过程[S i]可预测的时间尺度.同时根据最大Lyapunov指数,建立了高炉冶炼过程[S i]预报模型,取得了较好的结果.     

原油期货价格的混沌识别研究

系统复杂性的研究是系统工程的一个热点研究领域。在虚假邻域概念基础上,给出了合适的嵌入参数的确定方法。讨论了分形维与最大Lyapunov指数的计算方法。纽约市场国际原油期货收盘价格时间序列数据的计算表明:这些数据来源于一最大Lyapunov指数值为0.038的混沌吸引子,混沌吸引子分形维为3.625,需用4个变量描述其所在系统的运动规律。此结论为进一步利用混沌理论研究原油期货价格的运动规律、进行相关的投资决策提供了重要信息。     

基于多元混沌时间序列的油田产量预测模型

油田产量的预测一直是石油工作者研究的重要课题.针对油田产油量、产水量、地层压力和时间之间有着混沌的特征,利用多变量混沌时间序列等方法研究了油田产量的混沌建模和预测问题.用C-C算法确定每一个变量的嵌入维数和延迟时间,重构多元混沌时间序列的相空间;使用基于奇异值分解的主成分分析消除重构相空间的冗余变量和噪声干扰,建立了有较好泛化性能的多元混沌时间序列油田产量预测模型;最后将混沌时间序列预测和Elman神经网络进行耦合,创建了基于主成分分析前馈网络的多元混沌时间序列油田产量预测方法.应研究表明,提出的多变量混沌时间序列预测方法的预测精确度优于单变量预测,它可用于解决具有多变量混沌时间序列的预测问题.     

基于Volterra自适应方法的水文混沌时间序列预测

Volterra泛函级数能够描述具有响应和记忆功能的非线性行为,一般用于非线性系统因果关系点对的预测,把Volterra自适应方法应用于水文混沌时间序列的预测研究是一个有意义的工作。论文针对水文系统的复杂性,基于混沌动力系统相空间重构技术,构建了水文混沌时间序列Volterra自适应预测方法,并采用NLMS算法调整滤波器参数,并就模型进行仿真计算,讨论了模型参数对预测精度的影响。直门达水文站月蒸发量混沌时间序列预测实验表明,水文混沌时间序列Volterra自适应预测方法,具有较好的预测精度和效果,拓展了水文预测报方法的研究途径。     

中国股市价格和波动性的非线性行为实证研究

深沪综合指数的收益率不服从正态分布,收益率是负斜的,呈现胖尾和峰态;其收益率序列均服从有着分形概率分布的持久性时间序列,它们遵循有偏随机游动,市场表现出较强的趋势行为和非周期循环特征,深市非周期循环为4个月,而沪市为6个月,深沪股市月收益率序列则为确定性的混沌序列;而沪深股市的波动性也呈现出一定程度的不对称特征,沪深股市之间还存在着波动性的溢出效应,深市还存在着一定的杠杆效应,而且,沪深股市之间还存在着明显的波动性的溢出效应.     

基于Hurst指数的风速时间序列研究

为研究风速时间序列的长程相关性和自相似性,采用重标度极差分析和去趋势波动分析对风速时间序列进行相关性分析,计算风速时间序列的Hurst指数,并对其进行了功率谱密度分析,计算其谱指数.结果表明,两种方法计算所得Hurst指数都较为接近1,说明风速时间序列具有显著的自相似性和长程正相关性;但R/S分析及DFA所得Hurst指数有所差异,这一差异说明DFA可体现出非平稳风速时间序列的幂率特征.此外,对风速时间序列Hurst指数及谱指数的分析还表明了风速波动具有"1/f噪声"特征.为风速分形混沌特性研究及风速短时预测等提供了理论依据.     

基于支持向量机的混沌时间序列模型在房产价格指数预测中的应用

针对房产价格指数的预测问题,建立了混沌时间序列的支持向量机的非线性预测模型.首先运用Cao氏法进行相空间重构,并利用改进型小数据量法计算最大的Lyapunov指数,分析上海房产价格指数时间序列的混沌特性.然后以最小嵌入维数作为支持向量机的输入节点,建立房地价格指数的预测模型.实例表明,该方法能较好地处理复杂的房地产数据,具有较高的泛化能力和很好的预测精度.     

基于支持向量回归机的水文混沌时间序列预测

支持向量机在系统辨识和分类研究方面比较成熟,目前尚没有提出有效的支持向量回归理论来解决非线性、时变、干扰的复杂问题.支持向量回归机主要用于因果关系点对的回归预测,把支持向量回归机应用于水文混沌时间序列的预测研究是一个有意义的工作.在支持向量机一般理论基础上,提出了水文混沌时间序列支持向量回归机模型,并就模型进行仿真计算,讨论了模型参数对支持向量回归机预测精度的影响,为模型参数寻优提供一般指导原则.直门达水文站径流量混沌时间序列支持向量回归机预测实验表明,水文混沌时间序列支持向量回归机模型是有效的.     

太阳辐射时间序列的非线性检验

检验太阳辐射时间序列是否有非线性特征,对于分析、建模和预测太阳辐射量是重要、有益的.提出用基于替代数据的检验方法来检验太阳辐射时间序列是否存在非线性特征,并将数据序列的三阶矩作为检验统计量.选取了美国Montana州Dillon地区和Wyoming州Green Rivet地区每日总辐射量、Utah州Moab地区的每月日平均总辐射量时间序列作为检验对象.数值分析的统计结果表明所研究的日总辐射时间序列存在非线性,而每月日平均总辐射时间序列未检测出非线性.因而,对太阳辐射时间序列建模和预测之前,检验其是否有非线性特征是必要的.     

MODELING AND FORECASTING MONTHLY AVERAGE WATER LEVELS BASED ON THE ELLIPTIC ORBIT MODEL

Evaluation and forecasting of water‐level fluctuation for one river is of increasing importance since it is intimately associated with human welfare and socioeconomic sustainability development. In this study, it is found that time series of monthly water‐level fluctuation exhibits annual cyclical variation. Then with annual periodic extension for monthly water‐level fluctuation, the so‐called “elliptic orbit model” is proposed for describing monthly water‐level fluctuation by mapping its time series into the polar coordinates. Experiments and result analysis indicate potentiality of the proposed method that it yields satisfying results in evaluating and forecasting monthly water‐level fluctuation at the monitoring stations in the Yangtze River of China. It is shown that the monthly water‐level fluctuation is well described by the proposed elliptic orbit model, which offers a vivid approach for modeling and forecasting monthly water‐level fluctuation in a concise and intuitive way.     

南通地区月降水量时间序列分析

根据南通地区1989年-2005年月降水量数据,在统计检验其平稳性、纯随机性的基础上,结合谱分析,建立该地区具有季节效应的疏系数ARIMA月降水量时间序列模型,对模型作了拟合预测检验.研究表明,多个模型的联合使用比单一模型更利于准确拟合预测.     

FLOODING ON THE EEL RIVER: SYSTEM THEORETIC TIME SERIES,TRANSFER FUNCTION TIME SERIES,AND STRUCTURAL MODEL FORECASTS

Three models are developed to forecast precipitation, stream flow, and suspended sediment load for the Middle Fork Eel River basin near Dos Rios, California. The models are a structural model, a transfer function time series model, and a multivariate time series model based on the ideas of linear systems theory. Monthly observations from 1961 through 1970 were used to specify and estimate the models, and their performance was evaluated using 36 observations from 1971 through 1973. The comparisons show that all of the models are able to simulate the general trend of the data fairly well but that the two time series models capture the detail better. The system theoretic time series model fares the best, providing consistently better forecasts of all three series. We conclude that this model would be valuable for use in planning water supply systems in the Eel River basin.     

Extended and Unscented Kalman filtering based feedforward neural networks for time series prediction

With the ability to deal with high non-linearity, artificial neural networks (ANNs) and support vector machines (SVMs) have been widely studied and successfully applied to time series prediction. However, good fitting results of ANNs and SVMs to nonlinear models do not guarantee an equally good prediction performance. One main reason is that their dynamics and properties are changing with time, and another key problem is the inherent noise of the fitting data. Nonlinear filtering methods have some advantages such as handling additive noises and following the movement of a system when the underlying model is evolving through time. The present paper investigates time series prediction algorithms by using a combination of nonlinear filtering approaches and the feedforward neural network (FNN). The nonlinear filtering model is established by using the FNN’s weights to present state equation and the FNN’s output to present the observation equation, and the input vector to the FNN is composed of the predicted signal with given length, then the extended Kalman filtering (EKF) and Unscented Kalman filtering (UKF) are used to online train the FNN. Time series prediction results are presented by the predicted observation value of nonlinear filtering approaches. To evaluate the proposed methods, the developed techniques are applied to the predictions of one simulated Mackey-Glass chaotic time series and one real monthly mean water levels time series. Generally, the prediction accuracy of the UKF-based FNN is better than the EKF-based FNN when the model is highly nonlinear. However, comparing from prediction accuracy and computational effort based on the prediction model proposed in our study, we draw the conclusion that the EKF-based FNN is superior to the UKF-based FNN for the theoretical Mackey-Glass time series prediction and the real monthly mean water levels time series prediction.     

神经网络汛期降水短期气候预测模型

用1960-2005年每年5-9月安徽宣城7县(市)平均降水量作为预报对象,在对应降水前期逐月74项大气环流特征量资料、500hPa月平均高度场和月平均海温场资料中选取因子.采用主分量分析方法构造网络学习矩阵,降低矩阵维数,提高网络预测模型泛化性能.建立的神经网络汛期降水短期气候预测模型对历年样本拟合精度高,试报效果较好,可在气候预测业务中使用.     

Singular spectrum analysis based on the minimum variance estimator

In recent years Singular Spectrum Analysis (SSA), used as a powerful technique in time series analysis, has been developed and applied to many practical problems. In this paper, the SSA technique based on the minimum variance estimator is introduced. The SSA technique based on the minimum variance and least squares estimators in reconstructing and forecasting time series is also considered. A well-known time series data set, namely, monthly accidental deaths in the USA time series, is used in examining the performance of the technique. The results are compared with several classical methods namely, Box–Jenkins SARIMA models, the ARAR algorithm and the Holt–Winter algorithm.     

基于时间序列法的国税月度收入预测模型研究

研究了基于时间序列方法的国税月度收入预测. 通过采用Box-Jenkins的ARIMA模型, 结合国税月度收入数据, 分析并提出了一套针对月度税收收入的预测研究框架, 包括对税收预测模型的拟合、检验、预测、评价、动态修正等主要环节的处理方法. 在该研究框架的指导下, 以增值税、海关代征税和营业税为例, 对2006年各月的税收收入进行了模拟预测, 月度税收收入预测的平均相对误差分别控制在5.47\%, 8.63\%和2.37\%. 最后给出了在实际应用中动态修正税收预测模型的建议, 并简要讨论了时间序列方法在税收预测中面临的问题.     

ARIMA模型在新疆巴州梅毒疫情预测中的应用

搜集2008-2014年新疆巴音郭楞蒙古自治州(简称巴州)梅毒月发病数据,采用时间序列分解方法(Time Series Decomposition methods)探讨该地区梅毒月发病率的季节性,建立ARIMA(0,1,1)(1,0,1)_(12)时间序列模型,模型预测值的动态趋势与实际发病率基本吻合,平均绝对百分比误差MAPE=17.36,具有较高的预测精度,可以较好的预测短期内梅毒的变化趋势,为梅毒的预防控制措施提供可靠依据.