基于深度学习的节假日高速公路交通流预测方法

准确的预测节假日期间高速公路交通流量，能够为节假日高速公路应急管理提供重要的数据基础。利用深度学习的理论框架建立了LSTM-SVR 预测模型，利用BP 神经网络对样本数据进行处理，再将LSTM 捕获的数据特征输入SVR 回归层中实现交通流预测。选取“ 十一” 黄金周前后时段，利用位于丽江市的交调站流量监测数据对LSTM-SVR 模型进行验证，并将LSTM-SVR 模型与其它模型预测效果进行对比。发现LSTM-SVR 模型在节假日不同时段、天气、流量状态下的高速公路交通流预测中有较好的适用性。     

The total cost of misclassification in credit scoring: A comparison of generalized linear models and generalized additive models

This study examines whether the evaluation of a bankruptcy prediction model should take into account the total cost of misclassification. For this purpose, we introduce and apply a validity measure in credit scoring that is based on the total cost of misclassification. Specifically, we use comprehensive data from the annual financial statements of a sample of German companies and analyze the total cost of misclassification by comparing a generalized linear model and a generalized additive model with regard to their ability to predict a company's probability of default. On the basis of these data, the validity measure we introduce shows that, compared to generalized linear models, generalized additive models can reduce substantially the extent of misclassification and the total cost that this entails. The validity measure we introduce is informative and justifies the argument that generalized additive models should be preferred, although such models are more complex than generalized linear models. We conclude that to balance a model's validity and complexity, it is necessary to take into account the total cost of misclassification.     

水下航行器附连水质量的理论及实验研究

以二维无限长圆柱在水下的垂向振动为例,对其有限区域的不可压缩流场进行分析;通过数值计算并借鉴刘易斯法,分析了水域宽度比、水深比和浸没深度比等无量纲参数对刚体附连水质量的影响;提出了水下航行器附连水质量的快速预测公式.结果表明:刚性壁面可使刚体的附连水质量增大,但当刚体与壁面的距离增加到一定程度(截面半径的5倍)时,刚性壁面的影响可以忽略;自由表面可使刚体的附连水质量减小,当刚体与自由表面的距离达到截面半径的6倍时,可以忽略自由表面的影响;当刚体的垂向运动频率较高或刚体与自由表面的距离较大时,可以认为自由表面的速度势为零.     

An Adaptive Multiscale Ensemble Learning Paradigm for Nonstationary and Nonlinear Energy Price Time Series Forecasting

For forecasting nonstationary and nonlinear energy prices time series, a novel adaptive multiscale ensemble learning paradigm incorporating ensemble empirical mode decomposition (EEMD), particle swarm optimization (PSO) and least square support vector machines (LSSVM) with kernel function prototype is developed. Firstly, the extrema symmetry expansion EEMD, which can effectively restrain the mode mixing and end effects, is used to decompose the energy price into simple modes. Secondly, by using the fine‐to‐coarse reconstruction algorithm, the high‐frequency, low‐frequency and trend components are identified. Furthermore, autoregressive integrated moving average is applicable to predicting the high‐frequency components. LSSVM is suitable for forecasting the low‐frequency and trend components. At the same time, a universal kernel function prototype is introduced for making up the drawbacks of single kernel function, which can adaptively select the optimal kernel function type and model parameters according to the specific data using the PSO algorithm. Finally, the prediction results of all the components are aggregated into the forecasting values of energy price time series. The empirical results show that, compared with the popular prediction methods, the proposed method can significantly improve the prediction accuracy of energy prices, with high accuracy both in the level and directional predictions. Copyright © 2016 John Wiley & Sons, Ltd.     

Novelty,coherence, and Mendeleev’s periodic table

Predictivism is the view that successful predictions of “novel” evidence carry more confirmational weight than accommodations of already known evidence. Novelty, in this context, has traditionally been conceived of as temporal novelty. However temporal predictivism has been criticized for lacking a rationale: why should the time order of theory and evidence matter? Instead, it has been proposed, novelty should be construed in terms of use-novelty, according to which evidence is novel if it was not used in the construction of a theory. Only if evidence is use-novel can it fully support the theory entailing it. As I point out in this paper, the writings of the most influential proponent of use-novelty contain a weaker and a stronger version of use-novelty. However both versions, I argue, are problematic. With regard to the appraisal of Mendeleev’ periodic table, the most contentious historical case in the predictivism debate, I argue that temporal predictivism is indeed supported, although in ways not previously appreciated. On the basis of this case, I argue for a form of so-called symptomatic predictivism according to which temporally novel predictions carry more confirmational weight only insofar as they reveal the theory’s presumed coherence of facts as real.     

基于网络关注度的大宗商品市场预测研究

随着大宗商品市场化的加快和电子信息技术的快速发展,以互联网为载体的网络信息将方便快捷地传递到市场及市场参与者.本文从海量开源数据出发,利用搜索引擎平台,提取核心信息构建网络关注度指标,并提出了基于网络关注度的大宗商品价格预测模型.通过引入具有不同核函数的支持向量回归模型,分别建立了针对单个市场(原油、铜以及玉米)的网络关注度预测模型和综合考虑市场间联动性的多市场网络关注度预测模型.实证结果表明,网络关注度对于市场价格的变动有显著的格兰杰因果关系,引入网络关注度指标和相关市场信息能显著提高预测精度.     

网络结构特征与链路预测算法关系研究

以美国航空网络、科学家合作网络和线虫新陈代谢网络等5种实际网络为例进行了综合实验,用结果数据定量化描述了同配系数、集聚系数和网络效率等网络结构特征参数,与基于局部信息和全局信息的两类链路预测方法结果之间的关系。通过对结果的分析,得到了网络同配系数为正且聚集系数大于阈值(约0.1)时适用基于局部信息的预测方法,否则适用基于全局信息的预测方法;以及集聚系数、网络效率与局部信息预测方法的结果成正比,与全局信息预测方法成反比等结论。这些结论为通过网络特征参数进行链路预测方法的选择提供了定量化的参考依据。     

闭环回收供应链中牛鞭效应量化分析

环保和资源的问题关系到整个社会和环境,现代制造越来越注重资源的可持续化应用。将牛鞭效应的量化与控制引入闭环供应链的研究中,有效测量了有回收的供应链中的牛鞭效应。结合传统制造业的制造及分销渠道,运用运筹学及数学理论建立了一个由生产者、销售者及回收渠道组成的供应链模型,并进行数值实验计算和分析。结果表明:牛鞭效应仍存在于具有回收途径的供应链中,减少回收产品直接销售并重新加工能够降低整体供应链的牛鞭效应。     

BP神经网络在大学毕业生就业率预测中的研究与应用

为能更好掌握高校毕业生就业趋势,本文以佳木斯大学2002~2012年本科毕业生就业率为依据,利用神经网络方法,构建了BP神经网络预测模型.通过对所建模型训练学习可知,该网络最大误差为3.36%,满足预期要求.最后,对佳木斯大学2013-2015年本科毕业生就业率进行了预测,以期为相关部门工作提供参考依据.     

人口年龄结构对中国城乡居民消费率及消费差距的影响——基于省级面板数据的实证分析

在对中国人口年龄结构、消费率与城乡消费差距现状分析的基础上,基于2000-2014年中国省级面板数据,综合时空尺度,建立动态面板及空间面板数据模型,探究人口年龄结构对城镇消费率、农村消费率及城乡消费差距的影响.结果显示,少儿抚养比与老年抚养比对城镇居民消费率均具有正向影响,而对农村地区居民消费率则分别产生负效应和正效应;城乡少儿抚养系数比、城乡老年抚养系数比对城乡消费差距均具有正效应,且城乡居民消费差距存在空间集聚格局,呈现正的空间依赖性;"全面两孩"生育政策将改善人口年龄结构,从而有利于缩小城乡消费差距.