交通流灰色RBF网络非线性组合预测方法

针对智能交通系统的开发,提出一种基于灰色GM(1,1)模型和RBF网络非线性组合的短时交通流预测方法.该方法采用三层结构的RBF网络将两种单一预测方法(灰色GM(1,1)模型和RBF网络)进行了非线性组合.利用实测数据对组合方法进行了仿真实验,结果表明:非线性组合模型的预测准确性高于单独的RBF网络预测的准确性;组合模型发挥了两种单一方法各自的优势,是短时交通流预测的有效方法.     

基于关联面积的变权组合预测方法及其应用

变权组合预测方法是一种应用非常广泛的组合预测方法,它能够有效提高组合预测方法的预测和拟合精度.为了提高拟合精度,利用各个单项预测方法与原始数据序列在两个相邻时刻的关联面积以及关联度,给出了一种确定变权组合预测模型变权重系数的方法.最后利用该变权重的组合预测模型建立了新疆建设兵团城镇化发展水平的变权组合预测模型,结果表明这类变权组合预测方法具有较高的预测和拟合精度.     

证券投资预测的马氏链法和E-Bayes法

本文提出了两种证券投资预测方法-马氏链法和E-Bayes法.首先对数据进行分组,然后在此基础上应用马氏链法和E-Bayes法的理论建立预测模型,最后结合实际问题进行了计算,两种方法的预测结果是一致的.     

区域经济发展智能预测方法

分析了影响区域经济发展的各种因素,指出由于这些因素相互制约、相互影响,使得传统的经济预测方法越来越难以胜任区域经济发展预测的需要.论述了核方法在处理非线性、不确定性和不精确性数据上存在的优势,建立了基于核方法三种经济预测模型,并将这三种预测模型与其它两种预测方法一起,对区域经济的发展进行组合预测.最后,采用数据融合的方法将各个体模型的预测结果进行集成,作为最终的输出.实际的结果表明,基于核方法的组合预测技术能取得较为理想的预测效果.     

目标规划在大学生学习成绩预测中的应用

大学生学习成绩预测是非常有必要的。文章首先介绍了用最小二乘法进行线性回归预测的方法，并分析了其缺点，即当存在着极端的异常情况时，往往存在着较大的预测误差。其次文章介绍了另一种建立预测方程的方法——目标规划法，并详细分析了模型的建立过程。文章最后举了一实例，将两种方法进行了比较，结果证明，当存在着异常情况时，目标规划是一种较好的预测方法。     

核密度估计在预测风险价值中的应用

通过研究核密度估计理论,提出了一种适应估计金融时间序列分布的L ap lace核密度函数.在单变量核密度估计的基础上建立了风险价值(V a lua at R isk,简记为VaR)预测的预测模型.通过对核密度估计变异系数的加权处理建立了两种加权VaR预测模型.最后,通过上证指数收益率对建立的VaR预测模型进行了实证分析,结果显示两种加权方法对上证指数收益率的VaR预测具有较高的效率.     

四川省入境旅游需求组合预测研究

通过对四川省入境旅游人数的准确预测,可以加强政府部门对旅游行业的把控,引导旅游市场资源的合理利用与配置提供了技术支持.建立了4种预测模型,对四川省入境旅游人数进行预测.比较两个误差指标值,表明组合预测方法能够有效地提高预测精度.     

基于灰色系统和BP神经网络的股指动态预测

针对股价指数特有的波动性,提出了基于灰色残差模型和BP神经网络的股指动态预测方法,并运用多元线性回归模型对两种动态预测结果进行拟合.同时,随机抽取部分上证指数和道琼斯指数的实证研究表明:动态预测模型能及时调整新数据对后续预测的影响,获得了较高的预测精度.     

上市公司财务危机预警分析——基于数据挖掘的研究

本文以我国上市公司为研究对象,选取了1999-2001年被ST的公司和正常公司各73家作为训练样本,2002年被ST的公司和正常公司各43家作为检验样本,分析了财务危机出现前2年内各年两类公司15个财务指标。在进行数据挖掘中,我们运用了三种独立的方法,分别为判别分析、Logistic回归和神经网络,结果发现神经网络预测的效果要优于其它两种方法。最后,结合了这些方法的优点,建立了一种混合模型,研究表明预测的正确性要高于每种单独方法,从而提高了模型的预警效果。     

两种近似计算GM(1,1)参数的有效方法

给出了两种计算灰色模型参数的新方法,并证明通过这些方法所求的预测公式均具有白化指数律重合性,伸缩变换预测公式具有形式不变性,具有齐次性(或称具有伸缩变换一致性).并通过实例展示了方法的简易性和有效性.     

考虑信息泄露的零售商需求预测信息共享研究

针对信息泄露导致零售商信息共享意愿降低的问题,基于竞争型制造商创新的视角,构建了考虑信息泄露的零售商需求预测信息共享模型,求解了供应链各主体的均衡决策,分析了信息泄露对零售商信息共享的影响。研究发现:虽然需求预测信息泄露能够增加两个竞争型制造商的创新投入,但会削弱博弈主导制造商的领导者优势。上游竞争型供应链中的信息泄露会削弱下游零售商的事前利润,零售商的信息共享价值降低。本文克服了Shamir关于供应链需求信息泄露局限于零售商的不足,并进一步考虑了制造商之间的竞争和创新。     

Generalized GM (1, 1) model and its application in forecasting of fuel production

Grey theory is one approach that can be used to construct a model with limited samples to provide better forecasting advantage for short-term problems. Generally, the GM (1, 1) and Discrete GM (1, 1) models are two typical grey forecasting models in grey theory. However, there are two shortcomings in the above grey models respectively, i.e., the homogeneous-exponent simulative deviation in GM (1, 1) model, and the unequal conversion between the original and white equations in DGM (1, 1) model. In this paper, we firstly propose a novel Generalized GM (1, 1) model termed GGM (1, 1) model, based on GM (1, 1) and DGM (1, 1) models, to overcome the above shortcomings. Then, we detailedly study four important properties in this new grey model. Four estimative approaches of stepwise ratio in GGM (1, 1) model context is also covered. In the end, we simulate and forecast the fuel production in China during the period 2003–2010 using three GM (1, 1) models. The empirical results show that GGM (1, 1) model has higher simulative and predictive accuracy than GM (1, 1) and DGM (1, 1) models. This work contributes significantly to improve grey forecasting theory and proposes a optimized GM (1, 1) model.     

Adaptive Filtering: Once More with Gusto

Adaptive filtering, when used as a forecasting method, proposes to be able to distinguish a "signal pattern" of a time series instead of just smoothing out the random noise introduced by the data. Adaptive filtering is claimed by its creators to "...always do as well if not better than either moving averages, exponential smoothing,...". In order to see whether this claim could be substantiated, the author has taken the approach of a casual user of forecasting methods and has sought to determine whether adaptive filtering is useful, or not, as a forecasting method. The method was used to compute forecasts for ten sets of data on monthly insurance payments in a Finnish insurance company, and the experience gained from this work is compared with criticisms of the method expressed by a number of writers. It is shown that the method performs quite well for practical purposes, despite the fact that it has some major theoretical shortcomings.     

Combined grid-characteristic method for the numerical solution of three-dimensional dynamical elastoplastic problems

A combined method blending the advantages of smoothed particles hydrodynamics (SPH) and the grid-characteristic method (GCM) is proposed for simulating elastoplastic bodies. Various grid methods, including the GCM, have long been used for the numerical simulation of elastoplastic media. This method applies to the simulation of wave processes in elastic media, including elastic impacts, in which case an advantage is the use of moving tetrahedral meshes. Additionally, fracture processes can be simulated by applying various fracture criteria. However, this is a technically complicated task with the accuracy of the results degrading due to the continual updating of the grid. A more suitable approach to the simulation of processes involving substantial fractures and deformations is based on SPH, which is a meshless method. However, this method also has shortcomings: it produces spurious modes, and the simulation of oscillations requires particle refinement. Thus, two families of methods are available that are optimal as applied to two different groups of problems. However, a realworld problem can frequently be a mixed one, which requires a substantial tradeoff in the numerical methods applied. Aimed at solving such problems, a combined GCM-SPH method is developed that blends the advantages of two constituting techniques and partially eliminates their shortcomings.     

Forecasting demand for single-period products: A case study in the apparel industry

The problem considered is that of forecasting demand for single-period products before the period starts. We study this problem for the case of a mail order apparel company that needs to order its products pre-season. The lack of historical demand data implies that other sources of data are needed. Advance order data can be obtained by allowing a selected group of customers to pre-order at a discount from a preview catalogue. Judgments can be obtained from purchase managers or other company experts. In this paper, we compare several existing and new forecasting methods for both sources of data. The methods are generic and can be used in any single-period problem in the apparel or fashion industries. Among the pre-order based methods, a novel ‘top-flop’ approach provides promising results. For a small group of products from the case company, expert judgment methods perform better than the methods based on advance demand information. The comparative results are obviously restricted to the specific case study, and additional testing is required to determine whether they are valid in general.     

用时间序列方法预测股票价格初探

本文提出了描述股价变动的一种模型,讨论了模型建立、模型预测的一整套方法及改进算法,比较了算法与改进算法间的优劣,并通过实证分析说明整套理论有一定的可行性。大部分在本文中讨论的算法对于可用核模型描述的其他时间序列的预测问题也同样适用。     

An improved fuzzy time series forecasting method using trapezoidal fuzzy numbers

One of the major drawbacks of the existing fuzzy time series forecasting models is the fact that they only provide a single-point forecasted value just like the output of the traditional time series methods. Hence, they cannot provide a decision analyst more useful information. The aim of this present research is to design an improved fuzzy time series forecasting method in which the forecasted value will be a trapezoidal fuzzy number instead of a single-point value. Furthermore, the proposed method may also increase the forecasting accuracy. Two numerical data sets were used to illustrate the proposed method and compare the forecasting accuracy with three fuzzy time series methods. The results of the comparison indicate that the proposed method can generate forecasting values that are more accurate.     

Forecasting models: a comparison of several adaptive forecasting procedures

The object of the present investigation is to study and compare several adaptive forecasting methods. The present study consists of two parts. The adaptive forecasting models were selected under strict constraints on storage complexity. Included in the present study is the identification of the most robust and efficient adaptive forecasting procedures. The first part of the study consists of four methods which can be divided into three general classes: exponential smoothing, parameter switching and Kalman filtering

The results presented in this paper and the sequel, Part II, extend and correct the findings given by Bunn, [1]. The published results included several errors , including incorrect formulae, imprecise specification of initial conditions, and use of inadequate sample size. The present study considers data simultaneous from a very large set of underlying processes, including several for which exponential smoothing methods are not even quasi-optimal, in order to enhance the utility of the results in selecting suitable methods for forecasting

Part II of the present evaluations considers six additional forecasting procedures, including three which contain explicit corrections for first order autoregressive effects     

Application of classical transportation methods to find the fuzzy optimal solution of fuzzy transportation problems

To the best of our knowledge till now there is no method in the literature to find the exact fuzzy optimal solution of unbalanced fully fuzzy transportation problems. In this paper, the shortcomings and limitations of some of the existing methods for solving the problems are pointed out and to overcome these shortcomings and limitations, two new methods are proposed to find the exact fuzzy optimal solution of unbalanced fuzzy transportation problems by representing all the parameters as LR flat fuzzy numbers. To show the advantages of the proposed methods over existing methods, a fully fuzzy transportation problem which may not be solved by using any of the existing methods, is solved by using the proposed methods and by comparing the results, obtained by using the existing methods and proposed methods. It is shown that it is better to use proposed methods as compared to existing methods.