非线性时间序列预报的隐多分辨ARMA模型

研究一类用于非线性时间序列预报的隐多分辨自回归滑动平均(ARMA)模型,该模型以ARMA模型为初始细水平模型(即隐多分辨模型的基本块)．证明了模型的建模精度由水平问的方差决定．研究了新模型的自相关函数结构,给出了参数估计的Bayes方法和Metropolis-Hasting算法．进一步提出了一种可以直接用于不同基本块的隐多分辨模型的非线性时间序列预报方法,证明了其比其他的线性预报方法和隐多分辨模型预报方法降低了预报误差．最后通过数值模拟和实例验证了模型和预报方法,并和其他模型进行比较,结果表明新提出模型和预报方法能够更好地描述数据的特征,提高预报的精度．     

基于多尺度分析和神经网络的时间序列预报

本文提出一种由小波变换和神经网络相结合,进行时间序列预报的新方法。其中,小波作为滤波部分对原始序列进行多尺度分解,产生更容易建模和预测的子序列,再把上述子序列作为神经网络的输入进行时域预报。该方法考虑原时间序列的频率特性,采用不同的神经网络进行预报。     

天气系统的建模与预报

本文讨论了天气系统预报模型的建模和预报问题,说明了由于有了多层递阶预报方法,所以使得具有时变参数的线性预报模型在预报问题中具有一定的普遍性,并给出了应用效果的报告。     

多层递阶预报方法的改进及其在季节性时间序列预报中的应用

本文对多层递阶预报方法进行了深入的研究,提出了一种初值选取方法,并选择AR模型作为预报模型,采用高效的Houscholder变换来确定模型阶次,从而使得这种预报方法简便而实用,预报精度亦有进一步提高。本文还提出一种季节性时间序列预报的新方法,它具有简单、易行,且精度高的特点。本文将多层递阶预报方法成功地应用于冷库自动控制系统,实现了对温度的实时预报,取得了满意的效果。     

智慧洪水概率预报平台

利用最新的数值模拟和数理统计技术,研制洪水概率预报、预报不确定性降低控制和洪水风险评估等智能洪水预报模型。基于标准化链接、模型适配器技术和XML语言等平台开发新技术,创建多功能、多终端组件式洪水概率预报智慧平台,并在淮河进行实际应用,结果表明:洪水预报误差平均降低约6.2%,预报精度达90%,概率预报与数值天气预报耦合后,相同等级预报精度下洪水预见期延长3d以上,可为"控制洪水-洪水管理-洪水风险管理"理论与实践提供科技支撑。     

自校正预报开关控制器及其应用

本文给出两种自校正预报开关控制器,它是由自校正预报器和开关控制器组合而成的。在计算机控制系统中这种自校正预报开关控制器优于通常的开关控制器,具有一定的理论和应用价值。     

铁路客运量的预报

本文讨论铁路客运量的预报问题。介绍了一种直观、简便、实用的 AR 模型的建模方法。用该法所建的东北某站铁路客运量模型及其多步预报结果,在实用中具有一定的精度和满意度。     

农林作物产量的自适应预报

本文运用文〔1〕提出的“多层递阶预报方法”,通过适当选取预报系统的输入变量,并对人为因素进行适当的定量化,对主要粮豆作物的亩产量和水果产量进行了自适应预报。实践证明,效果良好。     

一种LSTM-BP多模型组合水文预报方法

水文数据是具有时序性的非线性数据，具有高度的不确定性和复杂性。使用单一模型进行预报的结果常常不尽人意，因此本文基于LSTM和BP神经网络建立LSTM-BP多模型组合预报模型进行水文预报。以子午河流域洪水数据为例进行预报，实验结果表明，多模型组合预报模型的预报结果要优于单一模型，同时预报的稳定性和精确度也得到了提高，从而为水文预报提供了新的思路。     

多模型多方法综合多层递阶预报模式在油田产量预报中的应用

本文介绍了多模型多方法综合多层递阶预报模式在油田产量预报中的应用情况,指出了这种模式在油田中应用的合理性。给出了适用于某油田的综合模式的模型族和算法族,报告了实际应用的结果。     

AN ADAPTIVE EXPECTATION GENETIC ALGORITHM BASED ON ANFIS AND MULTINATIONAL STOCK MARKET VOLATILITY CAUSALITY FOR TAIEX FORECASTING

The stock market is a highly complex and dynamic system, and forecasting stock is complicated and difficult. Successful prediction of stock prices may promise attractive benefits; therefore, stock market forecasting is important and of great interest. The economy of Taiwan relies on international trade deeply and the fluctuations of international stock markets impact Taiwan's stock market to certain degree. It is practical to use the fluctuations of other stock markets as forecasting factors for forecasting on the Taiwan stock market. Further, stock market investors usually make short-term decisions based on recent price fluctuations, but most time series models use only the last period of stock price in forecasting. In this article, the proposed model uses the fluctuations of other national stock markets as forecasting factors and employs an expectation equation method whose parameters are optimized by a genetic algorithm (GA) joined with an adaptive network–based fuzzy inference system (ANFIS) model to forecast the Taiwan stock index. To evaluate the forecasting performance, the proposed model is compared with Chen's model and Yu's model. The experimental results indicate that the proposed model is superior to the listing methods (Chen's model and Yu's model) in terms of root mean squared error (RMSE).     

基于模糊聚类的马尔可夫方法在需求预测中的应用

马尔可夫预测传统的状态划分采用人为确定方法,由预测者的经验决定预测对象的初始状态.状态界限的划分定性分析因素极大.提出根据预测对象数据自身的相似度,采用模糊聚类方法对预测对象进行初始状态划分,确定初始状态概率和状态转移概率矩阵,进行马尔可夫预测的方法.该改进的方法在某铸钢厂的铸坯需求预测中进行了应用,预测结果表明该方法可有效指导铸坯需求.     

INTUITIONISTIC FUZZY SETS BASED METHOD FOR FUZZY TIME SERIES FORECASTING

Fuzzy time series models are of great interest in forecasting when the information is imprecise and vague. However, the major problem in fuzzy time series forecasting is the accuracy of the forecasted values. In the present study we propose a hybrid method of forecasting based on fuzzy time series and intuitionistic fuzzy sets. The proposed model is a simplified computational approach that uses the degree of nondeterminacy to establish fuzzy logical relations on time series data. The developed model was implemented on the historical enrollment data for the University of Alabama and the forecasted values were compared with the results of existing methods to show its superiority. The suitability of the proposed method was also examined in forecasting market share prices of the State Bank of India on the Bombay Stock Exchange, India.     

带反馈的多元线性回归法在电力负荷预测中的应用

多元线性回归方法常用于电力负荷预测中,但是它不能预测非线性问题.我们把误差作为一个新的线性或者非线性的反馈自变量,用在下次的多元线性回归预测中.在理想实验环境下,我们发现这种方法是有效的.在电力负荷预测的实验中采用这种方法,得到了比用多元线性回归法更好的实验结果.     

A SHORT-TERM FORECASTING MODEL WITH INHIBITING NORMAL DISTRIBUTION NOISE OF SALE SERIES

In view of the dissatisfactory forecasting capability of standard support vector machine (SVM) for product sale series with normal distribution noises, a new SVM, called g-SVM, with Gaussian function used as its loss function, is proposed. It is theoretically proved that an adjustable parameter of g-SVM is equal to not only the upper bound of the proportion of erroneous samples to total samples but also the lower bound of the proportion of support vectors to total samples; in other words, the number of erroneous samples is fewer than or equal to that of support vectors. A new version of particle swarm optimization (PSO) with the integration of Logistic mapping and standard PSO is proposed for an optimal parameter combination of g-SVM. With the above, a short-term intelligent forecasting method based on g-SVM and the proposed PSO is then put forth. The results of its application to car-sales forecasting indicate that the forecasting method is feasible and effective.     

一种通用电力负荷预估方法及其系统实现

本文针对电力负荷预报系统中传统方法在普适性方面的不足,提出了一种基于多分辨率遗传算法的神经网络方法,并将其应用在短期电力负荷预报系统中,运行结果表明,该预报方法具有预报精度高、训练速度快,鲁棒性和适应性强等特点,使系统具有较强的通用性。     

AN ENHANCED DETERMINISTIC FUZZY TIME SERIES FORECASTING MODEL

The study of fuzzy time series has attracted great interest and is expected to expand rapidly. Various forecasting models including high-order models have been proposed to improve forecasting accuracy or reducing computational cost. However, there exist two important issues, namely, rule redundancy and high-order redundancy that have not yet been investigated. This article proposes a novel forecasting model to tackle such issues. It overcomes the major hurdle of determining the k-order in high-order models and is enhanced to allow the handling of multi-factor forecasting problems by removing the overhead of deriving all fuzzy logic relationships beforehand. Two novel performance evaluation metrics are also formally derived for comparing performances of related forecasting models. Experimental results demonstrate that the proposed forecasting model outperforms the existing models in efficiency.     

基于遗传算法的模糊神经网络股市建模与预测

提出一种基于模糊神经网络的股票市场建模与预测方法，并采用遗传算法训练网络权值及模糊子集的划分，对于上证指数及个股的建模与预测结果表明，该方法具有很强的学习与泛化能力，在处理诸如股票市场上这种具有一定程度不确定性的非互性的建模与预测方面有很发的价值。     

考虑气象因素的短期电力负荷预测研究

针对如何提高短期电力负荷预测精度的问题,提出基于核主成分分析法(Kernel principal component analysis,KPCA)和改进的回声状态网络(Echo state network,ESN)算法相结合的方法对短期电力负荷进行预测研究。通过卡尔曼滤波(Kalman filtering,KF)方法训练回声状态网络的输出权值,引入修正因子对卡尔曼滤波的协方差矩阵进行修正,从而实现回声状态网络结构参数的调整,获得理想的网络结构模型。采用Lyapunov理论验证了改进回声状态网络算法的收敛性。采用核主成分分析法对气象因素进行降维处理,获得能够体现数据信息的主元信息。通过UCI(University of California Irvine)数据集仿真对比,验证了该算法相比于ESN、SVM(Support vector machine)、BP(Back propagation)、GA(Genetic algorithm)等算法具有更高的预测精度。在考虑气象因素的前提下,对短期负荷预测进行仿真实验,实验结果显示在正常天气和存在气象突变的情况下,改进的回声状态网络算法较GA-ESN和GA-BP算法有更高的预测精度,验证了该方法的实用性。     

标准BP网络在降雨预报上的应用研究

本文研究了利用标准BP网络进行单站降雨预报。结果表明,神经网络在天气预报领域具有广阔的应用前景。文中探讨了神经网络的学习次数、拟事率、预报率三者之间的关系,并提出了进一步提高降雨预报准确率的几点设想。