Extracting behavioural models from 2010 FIFA world cup

The FIFA World Cup? is the most profitable worldwide event. The FIFA publishes global statistics of this competition which provide global data about the players and teams during the competition. This work is focused on the extraction of behavioural patterns for both, players and teams strategies, through the automated analysis of this dataset. The knowledge and models extracted in this work could be applied to soccer leagues or even it could be oriented to sport betting. However, the main contribution is related to the study on several automatic knowledge extraction techniques, such as clustering methods, and how these techniques can be used to obtain useful behavioural models from a global statistics dataset. The information provided by the clustering algorithms shows similar properties which have been combined to define the models, making the human interpretation of these statistics easier. Finally, the most successful teams strategies have been analysed and compared.     

Using the bootstrap to evaluate forecasting equations

The bootstrap, like the jack-knife, is a technique for estimating standard errors. The idea is to use Monte Carlo simulation, based on a non-parametric estimate of the underlying error distribution. The bootstrap will be applied to an econometric equation describing the demand for energy by industry, to determine multi-period forecasting error and choose among competing specifications. The delta method for estimating forecast errors turns out to be too optimistic by a factor of 2.     

Forecasting time series with increasing seasonal variation

Four options for modeling and forecasting time series data containing increasing seasonal variation are discussed, including data transformations, double seasonal difference models and two kinds of transfer function-type ARIMA models employing seasonal dummy variables. An explanation is given for the typical ARIMA model identification analysis failing to identify double seasonal difference models for this kind of data. A logical process of selecting one option for a particular case is outlined, focusing on issues of linear versus non-linear increasing seasonal variation, and the level of stochastic versus deterministic behavior in a time series. Example models for the various options are presented for six time series, with point forecast and interval forecast comparisons. Interval forecasts from data-transformation models are found to generally be too wide and sometimes illogical in the dependence of their width on the point forecast level. Suspicion that maximum likelihood estimation of ARIMA models leads to excessive indications of unit roots in seasonal moving-average operators is reported.     

行波：数学推理的元极导航

本书是《剑桥理论计算科学专论》丛书的第56卷。自从20世纪50年代起，自动化理论证明成为一个活跃的研究领域。在该领域中的研究人员开始着手处理类似人类的自动化推理。在20世纪60年代和70年代，人们对自动化理论证明的兴趣增加了，这是由于理论进展的驱动。例如归纳的开发以及对程序验证的兴趣增加。     

模拟结果为什么比实际更暖:简化气候模型结果（英文）

设计了一个非专业人士能够使用、最简化的气候敏感模型,用来研究人类活动所导致的全球变暖的幅度问题.在1990年的第一次IPCC评估报告中,IPCC对其报告中预测的未来全球变暖幅度很有信心,但是随后的观测结果显示全球的变暖幅度只有预测的一半.而自2001年起,全球变暖出现停滞,但是仅仅考虑到二氧化碳浓度的增加,很少有模型能够模拟出这一变化.在已出版的IPCC第五次评估报告的草稿中,IPCC大幅度削减了近期变暖的幅度,并以专家评估代替了模型预测.但是报告中关于未来气候长期变化的预测仍被保留.如果把IPCC模型的总反馈从1.9 W m–2 K–1调整到1.5 W m–2 K–1,气候敏感模型中模拟的温度将从3.2 K降至2.2 K.同时由于反馈很可能是净负反馈,更合适的估计应该是1.0 K.1.0 K是一个能够实现的增幅,21世纪的实际变暖将会小于1 K.即使燃烧所有可开采的化石燃料也不会使全球变暖的幅度超过2.2 K,这一增加幅度也将趋于平稳.本文认为解决IPCC第四、五次报告中评估方法的差异非常关键.一旦这些差异得到解决,人类活动导致的全球变暖在22世纪以及几个世纪以后的平稳态将有可能不会超过IPCC当前模型预测的1/3~1/2.     

Measuring and locating zones of chaos and irregularity

The new measures computed here are the spectral detrended fluctuation anatysls （sDFA） and spectral multi-taper method （sMTM）. sDFA applies the standard detrended fluctuation analysis （DFA） algorithm to power spectra, sMTM exploits the minute increases in the broadband response, typical of chaotic spectra approaching optimal values. The authors chose the Brusselator, Lorenz, and Duffing as the proposed models to measure and locate chaos and severe irregularity. Their series of chaotic parametric responses in short time-series is advantageous. Where cycles have only a limited number of slow oscillations such as for systems biology and medicine. It is difficult to create, locate, or monitor chaos. From 50 linearly increasing starting points applied to the chaos target function （CTF）; the mean percentage increases in Kolmogorov-Sinai entropy （KS-Entropy） for the proposed chosen models; and p-values when the models were compared statistically by Kruskal-Wallis and ANOVA1 test with distributions assumed normal are Duffing （CTF： 31%： p 〈0.03）; Lorenz （CTF： 2%： p 〈0.03）, and I3russelator （CTF： 8%： p 〈0.01）. Principal component analysis （PCA） is applied to assess the significance of the objective functions for tuning the chaotic response. From PCA the conclusion is that CTF is the most beneficial objective function overall delivering the highest increases in mean KS-Entropy.     

Classification Using Class Cover Catch Digraphs

class cover catch digraphs based on proximity between training observations. Performance comparisons are presented on synthetic and real examples versus k-nearest neighbors, Fisher's linear discriminant and support vector machines. We demonstrate that the proposed semiparametric classifier has performance approaching that of the optimal parametric classifier in cases for which the optimal is available for comparison.