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.     

利用朴素贝叶斯模型进行多层网络链接预测

针对多层网络链接预测中层间信息融合的问题，提出了一种利用朴素贝叶斯模型的链接预测方法。该方法结合目标层的邻域信息和辅助层相对于目标层的全局信息进行链接预测。在目标层中，根据节点对的邻域信息，利用朴素贝叶斯模型计算其连接概率；在辅助层中，计算节点对在该层有边或无边时在目标层存在链接的概率。在真实数据和合成数据上的实验结果表明：该算法在正相关和负相关的多层网络中都有很好的预测性能。     

Objectivity in confirmation: Post hoc monsters and novel predictions

The aim of this paper is to put in place some cornerstones in the foundations for an objective theory of confirmation by considering lessons from the failures of predictivism. Discussion begins with a widely accepted challenge, to find out what is needed in addition to the right kind of inferential–semantical relations between hypothesis and evidence to have a complete account of confirmation, one that gives a definitive answer to the question whether hypotheses branded as “post hoc monsters” can be confirmed. The predictivist view is then presented as a way to meet this challenge. Particular attention is paid to Worrall’s version of predictivism, as it appears to be the most sophisticated of the lot. It is argued that, despite its faults, his view turns our heads in the right direction by attempting to remove contingent considerations from confirmational matters. The demand to remove such considerations becomes the first of four cornerstones. Each cornerstone is put in place with the aim to steer clear of the sort of failures that plague various kinds of predictivism. In the process, it becomes obvious that the original challenge is wrongheaded and in need of revision. The paper ends with just such a revision.     

基于全基因组测序的粘绿木霉Gv29-8中分泌蛋白预测

粘绿木霉Gv29-8作为木霉属中重要的生防菌之一,对该菌分泌蛋白进行预测及其特征进行明确具有重要的理论意义。利用SignalP、ProtComp等预测程序对该菌中12427条蛋白质序列进行分泌蛋白找寻,并对上述分泌蛋白的氨基酸分布、信号肽长度大小及切割位点等性质进行分析。粘绿木霉含有分泌蛋白为377个,其氨基酸长度、信号肽长度与植物病原菌不同;信号肽切割位点属于A-X-A类型,与其他已经报道的植物病原真菌、卵菌中分泌蛋白信号肽切割位点一致。     

Nutch中网页更新预测研究与优化

Nutch的网页更新预测方法采用的是邻比法,相关更新参数需要人为设定,不能自适应调整,无法应对海量网页更新的差异性.为解决这个问题,提出动态选择策略对Nutch的网页更新预测方法进行改进.该策略在网页更新历史数据不足时,通过基于MapReduce的DBSCAN聚类算法来减少爬虫系统抓取网页数量,将样本网页的更新周期作为所属类其他网页的更新周期;在网页更新历史数据较多时,通过对网页更新历史数据进行泊松过程建模,较准确地预测每个网页的更新周期.最后在Hadoop分布式平台下对改进该策略测试.实验结果表明,优化后的网页更新预测方法表现更优.