基于可接受性标准的复杂产品仿真系统在线评估框架

针对当前复杂仿真系统评估的滞后性问题,提出了基于可接受性标准(Acceptability Criteria,AC)的仿真在线评估方法。通过定性和定量AC到指标的映射模型来建立评估指标体系;引入指标集和评估函数,提出了仿真评估流程有限自动机七元组模型,给出仿真流程到评估自动机的映射关系和基于数据驱动的状态转移机制,实现评估过程自动化执行过程;基于上述成果设计了在线评估工具,通过案例验证了在线评估方法的有效性,表明该方法能够有效解决仿真系统评估滞后性问题。     

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。     

物联网中区块链技术的应用与挑战

随着物联网技术的快速发展和广泛应用，分布式网络环境下的身份验证、数据隐私、网络安全等问题变得更加重要，而这些问题可以通过引入区块链技术来得到有效解决.该文简述了区块链的基本概念，介绍了区块链在物联网中的应用场景，并对当前一些相关工作的成果进行了综述；介绍了针对物联网场景下区块链底层技术的研究以及区块链物联网所面临的问题与挑战，希望能对未来区块链物联网的研究有一定的参考价值.     

计算Ulam映射高阶关联函数计算的数论方法

为计算logistic映射的高阶关联函数，本文提出了一个包含定义、引理及定理的数论方法，该方法的层次性结构有助于通过逐级求解丢番图方程来简化计算. 作为应用，本文计算了映射的前5阶关联函数. 6阶以上的关联函数也可以通过该方法直接算得.     

“压舱石”术语义商补

航运史上,空船往往必须加载压舱石,但加载压舱石的并不只有空船,《现代汉语词典》(第7版)“压舱石”释语中的释义成分“空船”宜改为“船只”。船用固体压舱物构成一个概念系统,其中“压舱石”和“压载铁(压舱铁)”的术语义在语义特征、使用语境和术语色彩上都有显著差异,“压舱石”释语中的释义成分“铸铁”宜删去。     

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.     

Can online search data improve the forecast accuracy of pork price in China?

Online search data provide us with a new perspective for quantifying public concern about animal diseases, which can be regarded as a major external shock to price fluctuations. We propose a modeling framework for pork price forecasting that incorporates online search data with support vector regression model. This novel framework involves three main steps: that is, formulation of the animal diseases composite indexes (ADCIs) based on online search data; forecast with the original ADCIs; and forecast improvement with the decomposed ADCIs. Considering that there are some noises within the online search data, four decomposition techniques are introduced: that is, wavelet decomposition, empirical mode decomposition, ensemble empirical mode decomposition, and singular spectrum analysis. The experimental study confirms the superiority of the proposed framework, which improves both the level and directional prediction accuracy. With the SSA method, the noise within the online search data can be removed and the performance of the optimal model is further enhanced. Owing to the long-term effect of diseases outbreak on price volatility, these improvements are more prominent in the mid- and long-term forecast horizons.