复杂仿真系统重用模型可信度评估方法

在资源高度共享的今天,模型重用可增强仿真应用的灵活性和可扩展性,是复杂仿真系统构建的必然选择。然而,评估多模型组合仿真下的可信性是模型能否实现有效重用的基本问题。针对面向实物和面向数值解算两个不同应用环境的仿真模型重用,提出基于偏差传播的仿真重用模型可信度评估方法,详细介绍了各自建模方式及评估方法。以无人机复杂仿真系统为例,通过与经典可信度评估方法对比,说明了该方法可降低在重用模型可信度方面的评估难度,验证了方法的可行性。     

序比例诱导分段无量纲化方法及其影响因素

为在评价数据的无量纲化处理过程中兼顾异常值情形，并减弱因异常值的存在而导致的无量纲化后数据之间分布不均衡的问题，在极值处理法的基础上，提出了一种非线性的无量纲化方法，即序比例诱导分段无量纲化方法。该方法以原始指标值的排序百分比为诱导变量对原始指标值进行分段，并在各区段内以极值处理法为基础分别进行无量纲化处理。通过性质分析，发现该方法能够较大程度地提升无量纲化结果对“总量恒定性”性质的满足程度。此外，采用模拟仿真的方法发现，该无量纲化方法对异常值具有较好的抗干扰性、且随着分段层级的增加其对异常值的敏感程度越来越低等主要结论。     

Predictive ability and economic gains from volatility forecast combinations

The availability of numerous modeling approaches for volatility forecasting leads to model uncertainty for both researchers and practitioners. A large number of studies provide evidence in favor of combination methods for forecasting a variety of financial variables, but most of them are implemented on returns forecasting and evaluate their performance based solely on statistical evaluation criteria. In this paper, we combine various volatility forecasts based on different combination schemes and evaluate their performance in forecasting the volatility of the S&P 500 index. We use an exhaustive variety of combination methods to forecast volatility, ranging from simple techniques to time-varying techniques based on the past performance of the single models and regression techniques. We then evaluate the forecasting performance of single and combination volatility forecasts based on both statistical and economic loss functions. The empirical analysis in this paper yields an important conclusion. Although combination forecasts based on more complex methods perform better than the simple combinations and single models, there is no dominant combination technique that outperforms the rest in both statistical and economic terms.     

Predicting exchange rates in Asia: New insights on the accuracy of survey forecasts

By linking measures of forecast accuracy as well as testing procedures with regard to forecast rationality this paper investigates aggregated survey forecasts with forecast horizons of 3, 12, and 24 months for the exchange rates of the Chinese yuan, the Hong Kong dollar, the Japanese yen, and the Singapore dollar vis-à-vis the US dollar and, hence, for four different currency regimes. The rationality of the exchange rate predictions is initially assessed utilizing tests for unbiasedness and efficiency which indicate that the investigated forecasts are irrational in the sense that the predictions are biased. As one major contribution of this paper, it is subsequently shown that these results are not consistent with an alternative, less restrictive, measure of rationality. Investigating the order of integration of the time series as well as cointegrating relationships, this empirical evidence supports the conclusion that the majority of forecasts are in fact rational. Regarding forerunning properties of the predictions, the results are rather mediocre, with shorter term forecasts for the tightly managed USD/CNY FX regime being one exception. As one additional important and novel evaluation result, it can be concluded, that the currency regime matters for the quality of exchange rate forecasts.     

Combining multivariate volatility forecasts using weighted losses

The ability to improve out-of-sample forecasting performance by combining forecasts is well established in the literature. This paper advances this literature in the area of multivariate volatility forecasts by developing two combination weighting schemes that exploit volatility persistence to emphasise certain losses within the combination estimation period. A comprehensive empirical analysis of the out-of-sample forecast performance across varying dimensions, loss functions, sub-samples and forecast horizons show that new approaches significantly outperform their counterparts in terms of statistical accuracy. Within the financial applications considered, significant benefits from combination forecasts relative to the individual candidate models are observed. Although the more sophisticated combination approaches consistently rank higher relative to the equally weighted approach, their performance is statistically indistinguishable given the relatively low power of these loss functions. Finally, within the applications, further analysis highlights how combination forecasts dramatically reduce the variability in the parameter of interest, namely the portfolio weight or beta.     

栎树猝死病在中国的入侵风险评估

【目的】栎树猝死病是一种为害林木和观赏植物的毁灭性病害,发病迅速,短期内即可造成寄主植物大量死亡,寄主范围非常广泛,主要为害阔叶树树种。对其在我国的适生区范围进行预测,并系统评估其入侵风险,有助于更好地制定针对性的防治及检疫措施。【方法】采用MaxEnt生态位模型,以栎树猝死病菌现有分布点的环境变量为基础运算预测模型,结合地理信息系统ArcGIS绘制其在中国的适生预测图;并以南京林业大学有害生物入侵预防与控制重点实验室建立的多指标综合评价体系为标准,从5个准则层下设18个指标层因子,对栎树猝死病菌在我国的入侵风险进行了定性和定量的分析。【结果】MaxEnt模型测试遗漏率与预测遗漏率基本吻合,ROC(receiver operating characteristic, ROC)曲线的AUC值(area under curver, AUC)为0.974,标准差为0.008,明显高于随机分布模型,说明该模型对栎树猝死病菌在我国的适生区预测结果可信度较高,可作为后续评估依据。栎树猝死病菌在中国的适生范围在101.9°~122.9° E,18.9°~38.0° N,主要位于我国秦岭淮河以南的南方地区,集中在长江中下游平原和武夷山脉、南岭以南的沿海地区,约占中国行政区划面积的19.6%。栎树猝死病菌在中国的入侵风险指标值R为2.64,属于极高风险的有害生物。【结论】模型预测结果的可信度较高,鉴于栎树猝死病菌在我国暂无分布记录,且在多指标评价体系中被归为极高风险等级,建议在进境检疫中对其可能寄主植物实施严格检疫及2年以上的隔离试种,防止其进入中国。     

新宁县可持续发展能力评价

为了评价新宁县可持续发展能力,根据县情建立了由经济发展、资源利用、生态建设与环境保护4个子系统组成的可持续发展能力评价指标体系,并采用全排列多边形综合图示法评价了2007–2014年的可持续发展现状以及2020的可持续发展水平。研究结果表明:2007–2012年,新宁县可持续发展综合指数均低于0.250,处于Ⅳ级较差水平;2013–2014年处于Ⅲ级一般水平;2020年达到0.945,进入Ⅰ级水平,可持续发展能力优良;当前新宁县可持续发展能力距离规划目标仍有很大差距。     

塔东热液地质作用机制及对储层的改造意义

塔东地区断裂发育，早期发育的断裂与晚期断裂之间存在相互改造、叠加、复合的过程，深大断裂沟通多套地层，断裂活动期形成的挤压破碎带和断裂裂缝带作为岩浆热液上升通道，为热液溶蚀作用改造储层提供了有利条件，因此，识别出热液期次对塔东白云岩储层成因机制具有重要意义。通过对钻井岩芯、岩屑及岩石薄片、铸体薄片观察鉴定分析，在区内下古生界碳酸盐岩中识别和发现了钠长石化作用和自生羽毛状伊利石沉淀，也是塔里木盆地内的首次发现，并通过对包裹体均一温度，碳氧同位素等地化特征研究，揭示其成岩流体源于岩浆热液，丰富了区内热液作用的矿物学标志，进一步识别出区内存在3期热液作用，通过对3期热液作用的机制及其储渗空间意义的研究，总结出第I期热液作用对于储层形成意义不大，第Ⅱ期热液作用主要发育在局限的沉积环境，第Ⅲ期热液溶蚀形成溶蚀缝孔洞，与天然气运移聚集匹配良好，构成了区内天然气聚集成藏的主要储渗空间。     

基于沉积数值模拟的辫状河心滩演化

选取黄骅坳陷新近系典型辫状河沉积解剖区,利用密井网区动静态资料相结合识别和刻画心滩及其内部结构,在此基础上通过沉积数值模拟探究研究区心滩形成过程,并建立研究区心滩顺流迁移和横向迁移演化模式。结果表明:顺流加积、垂向加积、侧向加积和侵蚀作用共同塑造心滩的形态和内部结构;滩头侵蚀、滩尾顺流加积和垂向加积形成了心滩的顺流生长和垂向生长;侧向加积与侵蚀作用控制心滩的不对称侧向生长过程。     

基于热中子探测评价裂缝的示踪剂特性研究

针对裂缝热中子示踪探测方法,从中子双组扩散理论入手结合蒙特卡洛数值模拟方法,分析压裂前后地层热中子分布影响因素,并模拟不同类型示踪剂及含量的热中子裂缝响应规律,筛选最佳示踪剂并给出其在支撑剂中含量的上、下限。结果表明:压裂前后热中子计数变化量主要受裂缝宽度和标记支撑剂中示踪剂含量的影响;钆对裂缝宽度变化反应最灵敏,钆元素是作为标记支撑剂的最佳示踪元素;随支撑剂中Gd 2O 3含量的增加,热中子计数率降低,热中子计数变化量D增加;示踪剂中氧化钆的上、下限值随裂缝宽度呈指数降低,且当裂缝宽度为1.0 cm时,Gd 2O 3的质量分数上限为0.68%,下限为0.03%。