基于混沌理论和PSO神经网络的短时交通流预测

交通流预测已成为智能交通的重要组成部分,针对短时交通流的非线性和不确定性,文中根据实际交通流中存在的混沌,利用C-C方法和小数据量法对交通流混沌进行了分析,在交通流混沌时间序列相空间重构的基础上构建了基于粒子群优化神经网络的单点单步预测模型,运用该模型对实际采集的美国加州城市快速路交通流数据进行了仿真研究,结果表明,该预测模型具有较高的预测精度,能够满足智能交通控制和诱导的需求。     

风电功率预测问题

本文主要针对风电本身固有的单位功率密度低、波动性和随机性强给接入电网运行的带来了不利影响这一问题,为了合理安排调度计划,保证电力系统稳定能运行,进一步加强电网对风电的接纳能力,利用混沌时间序列、BP神经网络、ARIMA、组合预测方法对风电输出功率进行预测,得出组合预测方法对风电功率的预测效果相对最佳。     

一种应用BP神经网络的混沌短期电力负荷预报方法

文章结合混沌理论及BP神经网络方法,提出了一种非线性时间序列预测算法。该方法基于相空间重构理论,将单维电力负荷时间序列映射到高维相空间中,并采用BP神经网络作为高维相空间拟合工具,通过算例的验证,证明了该方法具有较高的预测精度。     

公路运输货运量预测方法研究

介绍了国内外公路运输货运量的预测方法,分析各种预测方法的优缺点,以及实际的预测效果。针对公路运输货运量受很多相关因素的影响,使得现有的一些预测方法预测精度不高的问题,应用混沌神经网络,建立了公路运输货运量预测的混沌神经网络预测模型,并与传统的BP神经网络预测结果对比,表明此模型具有较好的预测效果。     

基于神经网络的药品需求组合预测研究与应用

药品的市场需求预测是制药企业生产控制中的重要组成部分,具有复杂的非线性特点。本文以制药企业的药品需求预测为研究对象,通过分析药品需求的特征,建立了基于神经网络的组合预测模型。本文选择3种具有互补特征的神经网络预测方法（BP神经网络的预测方法、RBF神经网络的预测方法和GRNN广义回归神经网络）分别对药品需求进行预测,然后在此基础上使用平均绝对相对误差（MAPE）为最优准则,通过求解二次规划问题得到权重并按照一定的规则进行变权,从而建立了基于神经网络的药品需求组合预测模型,最后对该模型实际应用的精度和稳定性进行评价。实验表明,本方法能够提高预测精度、稳定性,并扩大了模型的适用范围。     

基于ARIMA类模型的农业股票价格实证研究——以隆平高科为例

运用ARIMA-GARCH的模式来对中国股价波动作出预测，选择现代化农业代表企业隆平高科收盘价指数的时间序列作为研究对象，对该企业3年来股票收盘价进行分析，并利用ARIMA模型进行股价预测，同时加入波动性影响，利用GARCH模型对风险率建立模型，研究发现所选择的ARIMA-GARCH模型对收盘价时间序列具有较好的拟合作用，股票价格整体呈上升趋势，具有一定震荡性，但总体风险不大。     

基于主成分分析-RBF神经网络模型的备件预测研究

备件预测在产品物流保障中占有极其重要的地位,针对现有各种航空备件预测方法精度较低,无法满足实际需求的现状,文章提出了基于主成分分析-RBF神经网络模型的备件预测方法：首先利用主成分分析方法去除原始输入层数据的相关性,以解决RBF神经网络模拟预测备件需求时输入变量过多,网络规模过大导致效率下降的问题．最后选择合适的径向基函数密度训练神经网络。通过结合实例进行分析,取得了较好的效果。     

基于人工神经网络的时间序列分析方法在物流需求预测中的应用

当在对物流需求进行预测遇到较大波动的时间序列数据时,传统的以统计学为基础的预测方法在进行预测分析时误差会很大,本文建立了基于人工BP神经网络的预测方法并证明了其有效性。     

基于BP神经网络的MRO需求量预测模型研究

在分析MRO物料特点的基础上,利用BP神经网络算法,对MRO类物料历史需求数量的时间序列数据建立预测模型,解决了MRO类物料由于需求不稳定,影响因子难以确定等带来的需求量预测精度偏低的问题.并通过实证分析说明了BP神经网络在MRO类物料需求量预测上的有效性.     

房地产价格随机波动与房地产收益风险值(VaR)的研究

分析房地产价格的变化规律,科学准确地预测未来某一时点的价格,对房地产投资和国家针对房地产过热问题进行宏观调控具有重要指导意义。探讨了VaR方法在房地产收益波动性度量中的应用,并对上海住宅和办公楼价格指数时间序列收益率风险进行了实证研究;结果表明,SV模型能很好刻画价格指数实际特征,也能准确地预测房地产价格波动性。     

Linear models, smooth transition autoregressions, and neural networks for forecasting macroeconomic time series: A re-examination

In this paper, we examine the forecast accuracy of linear autoregressive, smooth transition autoregressive (STAR), and neural network (NN) time series models for 47 monthly macroeconomic variables of the G7 economies. Unlike previous studies that typically consider multiple but fixed model specifications, we use a single but dynamic specification for each model class. The point forecast results indicate that the STAR model generally outperforms linear autoregressive models. It also improves upon several fixed STAR models, demonstrating that careful specification of nonlinear time series models is of crucial importance. The results for neural network models are mixed in the sense that at long forecast horizons, an NN model obtained using Bayesian regularization produces more accurate forecasts than a corresponding model specified using the specific-to-general approach. Reasons for this outcome are discussed.     

Assessing the potential demand for electric cars

An ordered logit specification for use on ranked individual data is used to analyze survey data on potential consumer demand for electric cars. In many situations in economics and marketing we would like to be able to forecast consumer demands for goods which have not yet appeared in actual markets. By defining goods as a bundle of underlying attributes, we can use discrete choice models to estimate consumer evaluations. Then new good demand is forecast by use of the estimated coefficients to compare consumer evaluation of the new good to existing choices. When ranked individual data are available, we can estimate separate coefficients for each individual rather than assuming identical coefficients as is usual with logit models. Our results indicate considerable dispersion in individual coefficients. This finding can have important implications for new product analysis.     

物流基础设施网络节点的动态选址研究

物流基础设施网络节点的选址决策是一个长期决策,而随着时间的推移,需求和成本模式会随之变化,那么原来的选址决策就可能不是最优的,这时就需要确定一个随时间变化的选址方案,这个过程就是物流基础设施网络节点的动态选址.本文采用时间序列平滑预测法对需求进行预测,并据此用静态选址模型（重心法）得出结论,再运用动态规划技术,找出一个物流基础设施网络节点动态最优选址-再选址方案,使得计划期内的累积总利润现值最大化,并引入预测准确性因子来提高预测的准确性.     

多元回归时间序列方法在货运市场预测中的应用

为了解决制定某地区货运行业发展规划中的区域货运发展规模的分析和预测问题。提出一种以统计数据为依据,为货运行业需求量建立预测模型,并应用SPSS(Statistical Product Service Solution)分析软件,对预测结果进行评估检验的方法。最后以四川货运行业为例,研究预测了四川省今后数年的货运需求量,为四川货运部门进行决策提供一定的科学依据。     

Learning in Advance Selling with Heterogeneous Consumers

Advance selling occurs when consumers order a firm's product prior to the regular selling season. It reduces uncertainty for both the firm and the buyers and enables the firm to better forecast its future demand. The distinctive feature of this paper is that there are both experienced and inexperienced consumers, with the former knowing their valuations of the product in advance. We show that pre‐orders from experienced consumers lead to a more precise forecast of future demand by the firm and that the optimal pre‐order price may be at a discount or a premium relative to the regular selling price. Copyright © 2016 John Wiley & Sons, Ltd.     

Forecasting sales using online review and search engine data: A method based on PCA–DSFOA–BPNN

Traditional sales forecasting methods are mainly based on historical sales data, which result in a certain lag. The relationship between sales volume and its influencing factors is intricate and often non-linear. In view of this, we propose a novel product forecasting method using online reviews and search engine data. Firstly, a dictionary-based sentiment analysis method is developed to convert the textual review concerning each attribute of the product into the corresponding sentiment score. And by combining the prospect theory and relevant online review data, sentiment indices in each period are calculated. Subsequently, data of product-related Baidu search words with different lag orders are collected and screened by time difference correlation analysis. Finally, the forecast model, PCA–DSFOA–BPNN, is constructed by combining the principal component analysis (PCA), the back propagation neural network (BPNN), and the improved fruit fly optimization algorithm (DSFOA), in which sentiment indices, Baidu search data, and historical sales volume are input data. Taking the monthly sales forecast of 14 automobile models as a case study, we observe that the proposed forecast method can effectively improve the forecast accuracy with good robustness.     

A new error measure for forecasts of household-level,high resolution electrical energy consumption

As low carbon technologies become more pervasive, distribution network operators are looking to support the expected changes in the demands on the low voltage networks through the smarter control of storage devices. Accurate forecasts of demand at the individual household-level, or of small aggregations of households, can improve the peak demand reduction brought about through such devices by helping to plan the most appropriate charging and discharging cycles. However, before such methods can be developed, validation measures which can assess the accuracy and usefulness of forecasts of the volatile and noisy household-level demand are required. In this paper we introduce a new forecast verification error measure that reduces the so-called “double penalty” effect, incurred by forecasts whose features are displaced in space or time, compared to traditional point-wise metrics, such as the Mean Absolute Error, and p$p$-norms in general. The measure that we propose is based on finding a restricted permutation of the original forecast that minimises the point-wise error, according to a given metric. We illustrate the advantages of our error measure using half-hourly domestic household electrical energy usage data recorded by smart meters, and discuss the effect of the permutation restriction.     

煤炭物流集散中心流量预测研究

针对以往煤炭物流货运需求预测存在的局限性,研究构建以人工神经网络为主,以其它传统预测方法为辅的物流集散中心流量预测模型,用于对煤炭物流中心的年运输需求量进行预测。组合预测分析的实质是“信息的最大化利用”公理在预测分析中的一种体现,为决策与规划提供更多的信息支持。     

Long-term forecasting of fuel demand at theater entry points

Managing the distribution of fuel in theater requires Army fuel planners to forecast demand at the strategic level to ensure that fuel will be in the right place, at the right time, and in the amounts needed. This work presents a simulation approach to forecasting that accounts for the structure of the supply chain network when aggregating the demand of war fighters across the theater over the forecasting horizon. The resulting empirical distribution of demand at the theater entry point enables planners to identify forecast characteristics that impact their planning process, including the amplitudes and temporal positions of peaks in demand, and the estimated lead time to the point of use. Experimentation indicates that the forecasts are sensitive to the pattern of war fighter demand, the precise structure of the in-theater supply chain network, and the constraints and uncertainty present in the network, all of which are critical planning considerations.     

基于BP神经网络的最小库存量预测模型设计——以广东省某文具制造企业为例

文具制造行业的产品种类繁多,且需求变化大,库存水平的高低对于该行业中的企业有着重要的战略地位。由于企业实际的采购量制定的特殊性,以文具制造原材料纸张的最小实际库存为研究重点,用已有最小实际库存值及其影响因素的历史数据训练BP神经网络,然后用构建稳定的网络进一步预测某月的最小实际库存值,并且以此可以适当调整安全库存值。