应用多级Fisher判别准则预测二代玉米螟种群动态

本文论述了Ｆｉｓｈｅｒ准则下多级判别的基本原理，并应用该原理将山东省胶南市第二玉米螟百株累计卵量的３个影响因素压缩为一个新指标（线性判别函数），用这一个新指标对样品按距离最小准则判别归类。结果表明，历史拟合率为１００％，两年预报，其结果与实际一致。为害虫种群动态的预测预报提供了一种新的研究方法     

Non-parametric vs parametric forecasting in time series: A computational point of view

The finite sample behaviour of non-parametric predictors is considered for time series. Among other results, it is shown by simulation arguments that such predictors compare favourably with predictors based on parametric models in the spirit of the usual Box-Jenkins approach.     

Entropy-Weight-Method-Based Integrated Models for Short-Term Intersection Traffic Flow Prediction

Three different types of entropy weight methods (EWMs), i.e., EWM-A, EWM-B, and EWM-C, have been used by previous studies for integrating prediction models. These three methods use very different ideas on determining the weights of individual models for integration. To evaluate the performances of these three EWMs, this study applied them to developing integrated short-term traffic flow prediction models for signalized intersections. At first, two individual models, i.e., a k-nearest neighbors (KNN)-algorithm-based model and a neural-network-based model (Elman), were developed as individual models to be integrated using EWMs. These two models were selected because they have been widely used for traffic flow prediction and have been approved to be able to achieve good performance. After that, three integrated models were developed by using the three different types of EWMs. The performances of the three integrated models, as well as the individual KNN and Elman models, were compared. We found that the traffic flow predicted with the EWM-C model is the most accurate prediction for most of the days. Based on the model evaluation results, the advantages of using the EWM-C method were deliberated and the problems with the EWM-A and EWM-B methods were also discussed.     

High-order fuzzy time-series based on multi-period adaptation model for forecasting stock markets

Stock investors usually make their short-term investment decisions according to recent stock information such as the late market news, technical analysis reports, and price fluctuations. To reflect these short-term factors which impact stock price, this paper proposes a comprehensive fuzzy time-series, which factors linear relationships between recent periods of stock prices and fuzzy logical relationships (nonlinear relationships) mined from time-series into forecasting processes. In empirical analysis, the TAIEX (Taiwan Stock Exchange Capitalization Weighted Stock Index) and HSI (Heng Seng Index) are employed as experimental datasets, and four recent fuzzy time-series models, Chen’s (1996), Yu’s (2005), Cheng’s (2006) and Chen’s (2007), are used as comparison models. Besides, to compare with conventional statistic method, the method of least squares is utilized to estimate the auto-regressive models of the testing periods within the databases. From analysis results, the performance comparisons indicate that the multi-period adaptation model, proposed in this paper, can effectively improve the forecasting performance of conventional fuzzy time-series models which only factor fuzzy logical relationships in forecasting processes. From the empirical study, the traditional statistic method and the proposed model both reveal that stock price patterns in the Taiwan stock and Hong Kong stock markets are short-term.     

基于Lorenz模型的集合预报与单一预报的比较研究

根据非线性局部Lyapunov向量方法和增长模繁殖方法,选取Lorenz63模型和Lorenz96模型的不同状态为例,对集合预报与单一预报的预报技巧开展了对比研究.结果表明:与单一预报比较,集合预报的均方根误差和型异常相关有明显改善,随预报时间推移,改善效果越显著,且集合平均优于单一预报的实验个例数逐渐增多.就概率分布(f)而言,单一预报状态的f与真实状态基本一致,不随时间变化;而集合平均预报状态的f则随时间呈现出值域变窄、峰值变大的特点.表明随预报时间的延长,单一预报状态为混沌吸引子上的随机状态,而集合平均预报状态为吸引子子集上的随机状态,这可能是集合平均误差小于单一预报的原因.     

油价波动和资源环境补偿视角下石油资源税率研究——基于山东省数据

油价、资源与环境统筹管理政策缺失导致石油资源外部性问题日益严重,从油价波动和资源环境补偿视角出发研究石油资源税率是解决石油资源外部性,实现对油价、资源与环境统筹管理的关键。基于山东省2000~2013年石油资源开采行业数据,根据使用者成本法测算山东省石油开采行业的资源耗减成本,发现当前资源税费比率应提高14%;根据生态补偿标准模型测算生态环境成本,结合大量实地调研,指出生态环境补偿水平应从目前补偿水平的22%逐步提高至完全补偿生态环境成本。然后,根据价值对等原则构建资源生态环境补偿方程,探索性地计算了基于油价波动的石油资源税率,发现当油价处于(23~52)、(52~78)、(78~115)美元/桶的油价区间时,在7%折现率下分别对应17.51%、18.84%、19.04%的补偿税率,得出结论:当前条件下将资源税费与环境补偿费用合并征收,需要将补偿税率逐步提高至15%~20%。最后,从税率确定、计征方式以及监管体系三方面提出了执行对策。     

基于品牌价值的销售额预测模型应用研究

首先分析了企业的品牌价值与其销售额的变化情况,归纳出品牌价值与销售额之间的两个重要的变化规律.而后以此作为二者关系方程构建的基础假设,以广告反映模型(VW模型)为基础,将原模型中的广告投入变量替换为品牌价值变量,建立了品牌价值与销售额之间的关系方程.通过求解关系方程的平衡解,得到了以品牌价值为变量的销售额预测模型.最后,采用海尔集团的实际数据对所建模型的适用性和正确性进行了证明,同时还对模型的预测精度进行了检验.研究突破了以往研究一般采用时间、环境变量直接预测销售额,或是采用历史数据对销售额进行模拟预测等传统的预测方法,首次从品牌价值的角度出发研究其对销售额的影响途径及影响力度.品牌价值是能够包涵如广告投入等因素的全部信息量的重要指标,因此其能够更为全面客观地衡量商品在整个市场中的价值潜力、能够更准确地反映其未来的销售份额.     

我国未来能源投资需求预测及特点分析

持续的能源投资是经济持续发展的必要保障.鉴于我国能源投资的复杂性,基于系统动力学和成本预测方法,预测了我国2006-2050年期间的能源投资需求,分析了各能源品种的投资需求特点及不确定性因素.结果发现,我国能源投资将保持持续增长态势,但能源投资占GDP的比重不断下挫.而且,电力投资将一直占据我国能源投资的主导地位.     

航空装备战斗损伤概率预测模型研究

装备战场抢修是保持和恢复装备战斗力的重要因素.预测并准确掌握航空装备的损伤概率,有利于作战指挥员和装备保障人员适时而准确的指导战场抢修任务的完成,以保证航空装备的持续作战能力.通过分析航空装备的战伤原因,运用概率分析法构建了航空装备战斗损伤概率预测模型,并提出了模型的改进建议.     

灰色-马尔柯夫链预测优化模型——以江苏省物流需求预测为例

讨论了现有灰色-马尔柯夫链预测方法的基本思路,针对该思路的不足之处提出了合理刻画预测模型精度特征的两个精度指标——均值指标和稳定性指标,并据此建立了灰色-马尔柯夫链预测优化模型,最终以江苏省物流需求为例,对该模型进行了实例验证和应用.