多变量混沌时间序列的最小二乘支持向量机预测

文章根据多变量混沌时间序列的相空间重构理论,建立了多变量时间序列的最小二乘支持向量机预测模型.通过Lorenz系统和中国股市的股票价格序列对该模型进行了验证,结果表明该预测模型能精确地预测混沌时间序列,并且优于基于单变量时间序列的最小二乘支持向量机预测模型.     

基于支持向量机的降水量混沌时间序列预测

本文针对降水量时间序列的混沌性,根据混沌动力系统的相空间延迟坐标重构理论,基于支持向量机优越的非线性拟合性能,建立了基于支持向量机的降水量混沌时间序列预测模型。由于降水量时间序列的特殊性,本文采用均方根误差为标准来选取最优嵌入维数和模型参数,并结合实例验证该模型能精确地预测降水量。同时,这一结论也预示着支持向量机是一种研究混沌时间序列的有效方法。     

小波包与贝叶斯LS-SVM在石油价格预测中的应用

掌握国际石油价格变化趋势,可以为决策者提供决策依据.文章提出了基于小渡包和贝叶斯推断的最小二乘支持向量机石油价格预测方案.对石油价格时间序列进行小渡包分解与重构,采用贝叶斯推断对得到的各近似序列和各细节序列进行最小二乘支持向量机模型参数优化,再分别利用优化了的模型进行预测,合成得到最终预测结果.对美国纽约商品交易所原油价格进行仿真实验,结果表明该方法很好地改善了石油价格预测模型的运行速度与预测精度.     

基于LS-MWSVM的股票价格预测

文章基于小波分解理论和支持向量机核函数的条件,提出了最小二乘M0det小波核的支持向量机(LS-MWSVM)算法.用该算法建模并对沪深300日收盘价进行预测,且与常用的RBF核的LSSVM模型及RBF神经网络模型的预测能力进行了比较.结果表明.LS-MWSVM的预测能力要好于其它两种模型.进一步得出,采用最小二乘支持向量机与小渡理论结合的组合模型对股市进行预测效果较好.     

最小二乘支持向量机在条件异方差模型中的应用

相对于标准的支持向量机,最小二乘支持向量机是将求解二次规划问题转化为求解一组线性方程,从而能提高求解速度。将最小二乘支持向量机和GARCH模型相结合应用于金融时间序列预测中。通过在实际股票市场预测中的比较分析,能够证实所给方法是可行的、有效的。     

基于遗传算法优化的LS-SVM模型的应用研究

文章介绍了最小二乘支持向量机及遗传算法的原理,利用遗传算法优化参数后的最小二乘支持向量机建立四川省天然气消费量的时间序列预测模型.并利用两个性能指标将其与BP神经网络模型进行了对比,结果表明,在样本有限保证一定精度的情况下,遗传算法优化参数后的最小二乘支持向量机模型的范化能力较强,能够利用该模型对四川省天然气消费量进行预测,并在最后利用该模型预测2007～2009年四川省天然气消费量.     

LS-SVM的一种参数优选方法及混沌时间序列预测

支持向量机是一种新型的学习方法,该方法以结构风险最小化原则取代传统机器学习中的经验风险最小化原则,在小样本的机器学习中显示出了优异的性能.文章提出适当的验证性能指标用遗传算法优化最小二乘支持向量机的有关参数,并进行时间序列预测.通过对混沌时间序列的预测及和神经网络预测的比较证明,该模型的预测精确度是令人满意的,文中提出的方法是可行的.     

混沌时间序列的支持向量机预测

文章以重构相空间理论为基础,探讨了混沌时间序列的支持向量机预测模型建模的思路、特点及关键参数的选取;利用饱和关联维数法进行相空间重构,并运用小数据量法计算最大Lyapunov指数,对时间序列进行混沌特性识别。实例表明,该模型能较好地处理混沌时间序列,具有较高的泛化能力和很好的预测精度。     

基于SA和Bootstrap的LS-SVM参数优选及应用

文章针对最小二乘支持向量机的特点,通过Bootstrap建立适当的性能指标,用模拟退火算法(SA)优化最小二乘支持向量机的有关参数,并在非线性经济系统中应用.用最小二乘支持向量机对非线性经济系统进行预测的结果与神经网络预测的结果比较证明.该模型的预测精确度是令人满意的,文中提出的方法是可行的.     

一种基于RS-LSSVM的经济景气指数预测模型

为了消除经济景气指数系统中指标冗余及非线性预测困难等问题,文章利用粗糙集约简原理及支持向量机非线性预测特性,提出一种粗糙集与最小二乘支持向量机混合预测模型RS-LSSVM.模型运用粗糙集约简样本数据空间的维数,加快LSSVM的训练速度和模型的精度,又能弥补粗糙集方法在实际应用过程中噪声敏感问题.最后通过我国工业企业景气指数实证分析及与BP神经网络预测相比较,验证了该预测模型的有效性.     

奇异点检测的小波方法在证券市场中的应用

本文使用小波变换模极大值方法分析次贷危机中美国证券市场的突变。研究发现,小波模极大值方法准确定位了金融资产价格异常点的具体时刻;检测出了2类奇异点,其中峰值点检测比过零点检测更稳健;这些奇异点对应了美国次贷危机主要发展阶段的重大经济事件,反应出危机中美国经济系统异常对金融市场造成的影响。文章最后进行了稳健性检验。     

Detection of glitches and signal reconstruction using Hölder and wavelet analysis

We present a method based on a local regularity analysis for detecting and removing artefact signatures in noisy interferometric signals. Using Hölder and wavelet transform modulus maxima lines analysis (WTMML) [S. Mallat, W. Hwang, Singularities detection and processing with wavelets, IEEE Transaction on Information Theory 38 (1992) 617–643] in suitably selected regions of the time-scale half-plane, we can estimate the regularity degree of the signal. Glitches that are considered as a discontinuity on the signal show Hölder component lower than a fixed threshold defined for a continuous signal. After detection and signature removal, the signal is then locally reconstructed using Mallat reconstruction formulae. The method has been tested with Herschel SPIRE FTS proto-flight model calibration observations and shows remarkable results. Optimization of the detection parameters has been performed on the correlation coefficient, the scale domain for Hölder exponent estimation and reconstruction for SPIRE FTS signals.     

Confidence regions based on inefficient estimators

We consider an unbiased estimator of a function of mean value parameters, which is not efficient. This inefficient estimator is correlated with a residual vector. Thus, if a unit dispersion is unknown, it is impossible to determine the correct confidence region for a function of mean value parameters via a standard estimator of an unknown dispersion with the exception of the case when the ordinary least squares (OLS) estimator is considered in a model with a special covariance structure such that the OLS and the generalized least squares (GLS) estimator are the same, that is the OLS estimator is efficient. Two different estimators of a unit dispersion independent of an inefficient estimator are derived in a singular linear statistical model. Their quality was verified by simulations for several types of experimental designs. Two new estimators of the unit dispersion were compared with the standard estimators based on the GLS and the OLS estimators of the function of the mean value parameters. The OLS estimator was considered in the incorrect model with a different covariance matrix such that the originally inefficient estimator became efficient. The numerical examples led to a slightly surprising result which seems to be due to data behaviour. An example from geodetic practice is presented in the paper.     

A mixed-effects least square support vector regression model for three-level count data

Hierarchical study design often occurs in many areas such as epidemiology, psychology, sociology, public health, engineering, and agriculture. This imposes correlation in data structure that needs to be account for in modelling process. In this study, a three-level mixed-effects least squares support vector regression (MLS-SVR) model is proposed to extend the standard least squares support vector regression (LS-SVR) model for handling cluster correlated data. The MLS-SVR model incorporates multiple random effects which allow handling unequal number of observations for each case at non-fixed time points (a very unbalanced situation) and correlation between subjects simultaneously. The methodology consists of a regression modelling step that is performed straightforwardly by solving a linear system. The proposed model is illustrated through numerical studies on simulated data sets and a real data example on human Brucellosis frequency. The generalization performance of the proposed MLS-SVR is evaluated by comparing to ordinary LS-SVR and some other parametric models.     

股指期货信息内含股价变动信息的挖掘 ――小波框架与支持向量回归的金融建模应用

 内容提要：中国股指期货的推出指日可待，交易者多了一种投资工具的同时也带来了新的风险。建立准确的金融时间序列预测模型是逐利及避险的方法之一，一直是学者专家研究的热点。本研究结合小波转换与支持向量回归，提出一个二阶段时间序列预测模型。先以离散小波框架将预测变量分解成不同尺度的多个子序列，揭示隐藏在预测变量内的信息，再以支持向量回归为工具，以这些子序列为预测变量建构SVR模型。本研究以日经225指数开盘价为预测目标，以期货开盘价为预测变量对模型进行实证研究，结果显示，该模型的预测绩效比单纯SVR模型及随机漫步模型好。未来可尝试以不同的基底函数作进一步研究。     

灰色成分数据模型在中国产业结构分析预测中的应用

针对成分数据这种特殊类型的统计数据,提出一种新的预测建模方法:对于一列按照时间顺序收集的成分数据,先运用对数变换使成分数据降维,然后对降维后的数据运用GM(1,1)模型进行预测,最后再将预测值进行反对数变换,从而得到了各成分的预测值.根据提出的方法,建立了中国产业结构的预测模型,并分析了中国产业结构的发展趋势和未来状况.经检验,运用该方法预测出的数据与实际值十分吻合.     

A trimmed translation-invariant denoising estimator

A popular wavelet method for estimating jumps in functions is through the use of the translation-invariant (TI) estimator. The TI estimator addresses a particular problem, the susceptibility of the wavelet estimates to the location of the features in a function with respect to the support of the wavelet basis functions. The TI estimator reduces this reliance by cycling the data through a set of shifts, thus changing the relation between the wavelet support and the jump location. However, a drawback of the TI estimator is that it includes every shifted analysis in the reconstruction, even those that may reduce, rather than improve, the effectiveness of the method. In this paper, we propose a method that modifies the TI estimator to improve the jump reconstruction in terms of the mean squared errors of the reconstructions and visual performance. Information from the set of shifted data sets is used to mimic the performance of an oracle which knows exactly which are the best TI shifts to retain in the reconstruction. The TI estimate is a special case of the proposed method. A simulation study comparing this proposed method to the existing wavelet estimators and the oracle is provided.     

Measurement error in regression analysis

Consider the linear regression model Y = Xθ+ ε where Y denotes a vector of n observations on the dependent variable, X is a known matrix, θ is a vector of parameters to be estimated and e is a random vector of uncorrelated errors. If X'X is nearly singular, that is if the smallest characteristic root of X'X s small then a small perurbation in the elements of X, such as due to measurement errors, induces considerable variation in the least squares estimate of θ. In this paper we examine for the asymptotic case when n is large the effect of perturbation with regard to the bias and mean squared error of the estimate.