Research on the Method of Nonlinear Combining Forecasts based on Fuzzy System

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企业财务危机非线性组合预测方法及实证

针对组合预测比单项预测具有更高的预测精度,而线性组合预测方法在建模与预测方面存在着较大的局限性,提出了一种基于模糊神经网络的预测上市公司财务危机的非线性组合建模与预测方法,并给出了相应的混合学习算法。通过与多元线性回归模型、Fisher模型和Logistic回归模型的预测结果对比表明,该方法具有预测精度高、学习与泛化能力强和适应性广的优点。     

Forecasting Container Throughput of Qingdao Port with a Hybrid Model

This paper proposes a hybrid forecasting method to forecast container throughput of Qingdao Port.To eliminate the influence of outliers,local outlier factor(lof) is extended to detect outliers in time series,and then different dummy variables are constructed to capture the effect of outliers based on domain knowledge.Next,a hybrid forecasting model combining projection pursuit regression(PPR) and genetic programming(GP) algorithm is proposed.Finally,the hybrid model is applied to forecasting container throughput of Qingdao Port and the results show that the proposed method significantly outperforms ANN,SARIMA,and PPR models.     

用神经网络组合预测法估算反舰导弹研制费用

运用BP神经网络组合预测法对反舰导弹研制费用进行估算。首先分别利用神经网络和回归分析方法对导弹研制费用进行估算 ,然后利用BP网络对所得结果进行组合预测。计算实例表明 ,使用BP神经网络组合预测方法所得的预测结果比单一使用神经网络或回归分析方法所得结果总体误差要小 ,因而该方法是可行的、有效的。     

调和平均组合预测中的参数估计技术

本文从几何距离角度出发，研究调和平均组合预测中的参数估计技术，提出三种确定组合预测权系数的新方法。理论分析和预测实例表明，新的组合预测参数估计技术行之有效。     

An Ensemble Model of Arima and Ann with Restricted Boltzmann Machine Based on Decomposition of Discrete Wavelet Transform for Time Series Forecasting

Time series forecasting research area mainly focuses on developing effective forecasting models to improve prediction accuracy. An ensemble model composed of autoregressive integrated moving average (ARIMA), artificial neural network (ANN), restricted Boltzmann machines (RBM), and discrete wavelet transform (DWT) is presented in this paper. In the proposed model, DWT first decomposes time series into approximation and detail. Then Khashei and Bijari’s model, which is an ensemble model of ARIMA and ANN, is applied to the approximation and detail to extract their both linear and nonlinear components and fit the relationship between the components as a function instead of additive relationship. Furthermore, RBM is used to perform pre-training for generating initial weights and biases based on inputs feature for ANN. Finally, the forecasted approximation and detail are combined to obtain final forecasting. The forecasting capability of the proposed model is tested with three well-known time series: sunspot, Canadian lynx, exchange rate time series. The prediction performance is compared to the other six forecasting models. The results indicate that the proposed model gives the best performance in all three data sets and all three measures (i.e. MSE, MAE and MAPE).     

论施肥系统工程

<正> 施肥是作物增产的重要手段。尤其是化肥施用,已成为现代农业生产中一项重要的技术措施。据联合国粮农组织以及各国专家的估计,粮食增产量中,30％～60％应归功于化肥的施用。中国农科院土壤肥料研究所张夫道等统计分析了我国解放后三十年农作物产量后指出,我国粮棉总产水平     

用神经网络组合预测法估算反舰导弹研制费用

运用BP神经网络组合预测法对反舰导弹研制费用进行估算。首先利用回归分析法建立三元线性回归模型对导弹研制费用进行估算,然后利用3层前馈型BP神经网络对导弹研制费用进行估算,最后利用人工神经网络中的BP网络对所得结果进行组合预测。计算实例表明,使用该组合预测方法所得的预测结果比单使用神经网络或回归分析方法所得结果的总体误差要小,因而该方法是可行而有效的。     

广义加权比例平均组合预测技术研究

本文提出了一类广义加权比例平均组合预测技术。新的组合预测技术比传统的组合预测技术更具有优越性,能取得更好的组合预测效果。     

短时交通流量智能组合预测模型及应用

提出了一种新的短时交通流量智能组合预测模型.该智能组合模型包含三个子模型:卡尔曼滤波模型、人工神经网络模型和模糊综合模型.卡尔曼滤波模型利用卡尔曼滤波方法良好的静态线性稳定特性,采用线性迭代方式对交通流量进行最优估计.人工神经网络模型利用其强大的动态非线性映射能力,对动态交通流量的预测具有较高的精度和满意度.模糊综合模型采用模糊方法来综合这两个单项模型的输出,并把它的输出作为整个组合模型的最终交通流量预测值.实际应用表明:该组合模型的预测精度高于单项预测模型各自单独使用时的精度,发挥了两种模型各自的优势,是短时交通流预测的一种有效方法.     

基于串联灰色神经网络的电力负荷预测方法

为了提高电力负荷预测的精度,分析现有人工神经网络和灰色预测方法各自的优缺点,将二者相结合提出了一种串联灰色神经网络预测方法.新方法利用灰色预测中的累加生成运算对原始数据进行变换,从而得到规律性较强的累加数据,便于神经网络进行建模和训练,同时避免了灰色预测方法存在的理论误差.最后实际算例证明了方法的有效性.方法适用于中长期负荷预测.     

A Demand Forecasting Method Based on Stochastic Frontier Analysis and Model Average: An Application in Air Travel Demand Forecasting

Demand forecasting is often difficult due to the unobservability of the applicable historical demand series. In this study, the authors propose a demand forecasting method based on stochastic frontier analysis(SFA) models and a model average technique. First, considering model uncertainty,a set of alternative SFA models with various combinations of explanatory variables and distribution assumptions are constructed to estimate demands. Second, an average estimate from the estimated demand values is obtained using a model average technique. Finally, future demand forecasts are achieved, with the average estimates used as historical observations. An empirical application of air travel demand forecasting is implemented. The results of a forecasting performance comparison show that in addition to its ability to estimate demand, the proposed method outperforms other common methods in terms of forecasting passenger traffic.     

2000年中国轿车保有量预测—中国轿车工业发展浅析

2000年中国轿车保有量与我国石油需求量密切相关。本文提出一种国际类比预测方法, 即根据轿车保有量与人均GNP之间存在的普遍规律, 预测2000年中国轿车保有量。计算结果说明这种预测方法和模型是合理的、有效的。预测结果对我国能源需求预测有重要意义。     

Time Series Neural Network Forecasting Methods

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CRUDE OIL PRICE FORECASTING WITH TEI@I METHODOLOGY

The difficulty in crude oil price forecasting, due to inherent complexity, has attracted much attention of academic researchers and business practitioners. Various methods have been tried to solve the problem of forecasting crude oil prices. However, all of the existing models of prediction can not meet practical needs. Very recently, Wang and Yu proposed a new methodology for handling complex systems-TEI@I methodology by means of a systematic integration of text mining, econometrics and intelligent techniques.Within the framework of TEI@I methodology, econometrical models are used to model the linear components of crude oil price time series (i.e., main trends) while nonlinear components of crude oil price time series (i.e., error terms) are modelled by using artificial neural network (ANN) models. In addition, the impact of irregular and infrequent future events on crude oil price is explored using web-based text mining (WTM) and rule-based expert systems (RES) techniques. Thus, a fully novel nonlinear integrated forecasting approach with error correction and judgmental adjustment is formulated to improve prediction performance within the framework of the TEI@I methodology. The proposed methodology and the novel forecasting approach are illustrated via an example.     

基于贝叶斯理论的日用水量概率预测

为解决城市日用水量的概率预测问题,提出了基于贝叶斯理论的日用水量预测法.引入贝叶斯理论,建立了日用水量概率预测系统.在系统中,利用支持向量机建立日用水量预测模型、似然函数和先验密度,并采用自适应马尔可夫链蒙特卡罗模拟方法求解日用水量的后验密度,得到日用水量的概率预测值.实例表明,本文提出的预测方法不仅显著提高了日用水量的预测精度,而且通过定量给出预测值的置信区间,为城市供水系统的调度提供了更科学、可靠的决策依据.     

广义加权多重函数平均组合预测及其参数估计

提出一种新的组合预测模型———广义加权多重函数平均组合预测模型,并利用二次规划算法给出其加权系数的不同参数估计方法。该模型具有广泛的代表性,它集多种组合预测模型于一体,是一种新的群组集结方法,通过选择合适的模型组合形式及最佳的模型参数,能够有效地提高预测精度。预测实例表明了该模型的有效性。     

降水时间序列的聚类分析和预测

本文阐述了对160个中国大陆降雨序列进行同步预测的完整方法。预测模型是用多元分析和随机序列等方法综合建立的, 预测结果较好。文中重点研究能用个人计算机可方便地同时预测这160个点的方法。因此, 首先采用聚类方法将160个点的降水序列分成若干个局部特征相似的几个子类。采用该法的分类结果与国内大多数着名专家的分类结果相似。采用数据压缩法求出每个子类周期性特征的主成分序列, 计算中在精度损失不大情况下尽量压缩计算量。该模型预测一年后的降雨量, 获得令人满意的结果。     

A Practical Qualitative＋Quantitative Method—S—ANN

1　 IntroductionPurely quantitative methods are in a dilemma situation for coping with the real worldproblem,particularly with respectto social and economic problems. The reason is thatthemotivation of pursuing science requires researchers to describe these complex socialproblem accurately,then solve these quantitatively mathematical models so as to discoverthe rules of system movement and furthermore forecast the behaviors of system in thefuture.On the otherhand,these social problemsofthe rea…     

Foreign Trade Survey Data: Do They Help in Forecasting Exports and Imports?

Business survey, which starts from the microeconomic level, is a widely used short-term forecasting tool in practice. In this study, the authors examine whether foreign trade survey data collected by China’s Ministry of Commerce would provide reliable forecasts of China’s foreign trade.The research procedure is designed from three perspectives including forecast information test, turning point forecast, and out-of-sample value forecast. First, Granger causality test detects whether survey data l...