地区电力需求预测研究

本文在分析影响预测准确原因的基础上．指出预测工作必须定量与定性相结合．针对地区电力消费需求的特点，提出了适宜于定性分析的四个预测模型．运用上述模型，实际预测结果令人满意。     

基于复合模型的智能化移动通信话务预测

移动通信话务量作为一种时间序列,具有较强的非线性和随机性,而且易受节假日、旅游等客户行为及天气等其它因素的影响。尤其是话务量长期的发展变化,很难用传统的预测方法进行预测。根据移动通信话务量自身特点,采用复合模型,将话务量分为平稳期趋势分量、平稳期周期分量、节假日话务量,用综合评判的分段一元线性回归及模板匹配算法分别对趋势分量、周期分量和节假日话务进行建模。最后,开发了基于复合模型的智能化预测系统,在广东省某市试运行的结果表明：基于复合模型的预测方法比传统预测方法精度高。     

Approximate importance sampling Monte Carlo for data assimilation

Importance sampling Monte Carlo offers powerful approaches to approximating Bayesian updating in sequential problems. Specific classes of such approaches are known as particle filters. These procedures rely on the simulation of samples or ensembles of the unknown quantities and the calculation of associated weights for the ensemble members. As time evolves and/or when applied in high-dimensional settings, such as those of interest in many data assimilation problems, these weights typically display undesirable features. The key difficulty involves a collapse toward approximate distributions concentrating virtually all of their probability on an implausibly few ensemble members.

After reviewing ensembling, Monte Carlo, importance sampling and particle filters, we present some approximations intended to moderate the problem of collapsing weights. The motivations for these suggestions are combinations of (i) the idea that key dynamical behavior in many systems actually takes place on a low dimensional manifold, and (ii) notions of statistical dimension reduction. We illustrate our suggestions in a problem of inference for ocean surface winds and atmospheric pressure. Real observational data are used.     

Adaptive modeling, adaptive data assimilation and adaptive sampling

For efficient progress, model properties and measurement needs can adapt to oceanic events and interactions as they occur. The combination of models and data via data assimilation can also be adaptive. These adaptive concepts are discussed and exemplified within the context of comprehensive real-time ocean observing and prediction systems. Novel adaptive modeling approaches based on simplified maximum likelihood principles are developed and applied to physical and physical–biogeochemical dynamics. In the regional examples shown, they allow the joint calibration of parameter values and model structures. Adaptable components of the Error Subspace Statistical Estimation (ESSE) system are reviewed and illustrated. Results indicate that error estimates, ensemble sizes, error subspace ranks, covariance tapering parameters and stochastic error models can be calibrated by such quantitative adaptation. New adaptive sampling approaches and schemes are outlined. Illustrations suggest that these adaptive schemes can be used in real time with the potential for most efficient sampling.     

基于GA-BP的敌空袭兵力出动量预测

在防空兵力资源有限的情况下,为取得最大的对敌作战效能,有必要对敌空袭兵力出动量进行预测.在遗传算法与误差反向传播神经网络结构模型相结合的基础上,设计了用遗传算法训练神经网络权重的新方法,并对敌对地面目标群空袭的兵力出动量进行预测,同时与BP算法和灰色系统理论模型进行了比较.经检验,计算值与实际值接近,并优于BP算法和灰色理论模型,具有良好的预测效果.     

Methods of rapid orbit forecasting after maneuvers for geostationary satellites

A geostationary (GEO) satellite may serve as a navigation satellite, but there is a problem that maneuvers frequently occur and the forces are difficult to model. Based on the technique of determining satellite orbits by transfer, a predicted orbit with high accuracy may be achieved by the method of statistical orbit determination in case of no maneuver force. The predicted orbit will soon be invalid after the maneuver starts, and it takes a long time to get a valid orbit after the maneuver ends. In order to improve ephemeris usability, the method of rapid orbit forecasting after maneuvers is studied. First, GEO satellite movement is analyzed in case of maneuvers based on the observation from the orbit measurement system by transfer. Then when a GEO satellite is in the free status just after maneuvers, the short arc observation is used to forecast the orbit. It is assumed that the common system bias and biases of each station are constant, which can be obtained from orbit determination with long arc observations. In this way, only 6 orbit elements would be solved by the method of statistical orbit determination, and the ephemeris with high accuracy may be soon obtained. Actual orbit forecasting with short arc observation for SINOSAT-1 satellite shows that, with the tracking network available, the precision of the predicted orbit (RMS of O-C) can reach about 5 m with 15 min arc observation, and about 3 m with 30 min arc observation. Supported by the National High Technology Research and Development Program of China (Grant No. 2006AA12Z322), the National Basic Research Program of China (Grant No. 2007CB815503), and the West Light Program of Chinese Academy of Sciences (Grant No. 2007LH01)     

The pricing and optimal strategies of callable warrants

In this paper, we introduce a valuation model of callable warrants under a setting of the optimal stopping problem between the holder (investor) and the issuer (firm). A warrant is the right to purchase new shares at a predetermined price. When the new stocks are issued, the value of the stock is diluted. We consider the model taking the dilution into account. After identifying optimal policies for the issuer and the investor, we explore the analytical properties of the optimal exercise and call boundaries for the holder and the issuer, respectively. Furthermore, the value of such a callable warrant and the optimal critical prices are examined numerically using the binomial method.     

物价、利率与收入对居民消费需求影响研究——基于时变参数状态空间模型

运用时变参数状态空间模型对我国改革开放三十年来物价、利率与收入对农村和城镇居民消费需求影响的动态特征进行了研究。发现物价、收入对农村和城镇居民的消费需求弹性不同,农村消费需求受收入影响较大,而城市居民消费需求受物价影响较大;利率对农村和城镇居民消费需求影响不显著,利率机制目前还不是调解中国消费需求的理想工具。在此基础上给出了相应的政策建议。     

Forecasting cancellation rates for services booking revenue management using data mining

Revenue management (RM) enhances the revenues of a company by means of demand-management decisions. An RM system must take into account the possibility that a booking may be canceled, or that a booked customer may fail to show up at the time of service (no-show). We review the Passenger Name Record data mining based cancellation rate forecasting models proposed in the literature, which mainly address the no-show case. Using a real-world dataset, we illustrate how the set of relevant variables to describe cancellation behavior is very different in different stages of the booking horizon, which not only confirms the dynamic aspect of this problem, but will also help revenue managers better understand the drivers of cancellation. Finally, we examine the performance of the state-of-the-art data mining methods when applied to Passenger Name Record based cancellation rate forecasting.     

生产计划中阶梯型价格原料最优配置

按照规模经济效应,商品价格会随着订购量的增大而减小(即打折).根据生产实际,本文提出了商品的阶梯型价格,建立了最优生产计划模型.该计划模型有选择性地生产能够获最大利润的产品,并且可以对生产所需的原料进行最优配置;基于最优化理论与方法,给出了求解所建模型的有效算法,通过实例验证了算法的可行性和有效性.