Predicting Stock Prices Using Polynomial Classifiers: The Case of Dubai Financial Market

Predicting stock price movements is a challenging task for academicians and practitioners. In particular, forecasting price movements in emerging markets seems to be more elusive because they are usually more volatile often accompa-nied by thin trading-volumes and they are susceptible to more manipulation compared to mature markets. Technical analysis of stocks and commodities has become a science on its own;quantitative methods and techniques have been applied by many practitioners to forecast price movements. Lagging and sometimes leading technical indicators pro-vide rich quantitative tools for traders and investors in their attempt to gain advantage when making investment or trading decisions. Artificial Neural Networks (ANN) have been used widely in predicting stock prices because of their capability in capturing the non-linearity that often exists in price movements. Recently, Polynomial Classifiers (PC) have been applied to various recognition and classification application and showed favorable results in terms of recog-nition rates and computational complexity as compared to ANN. In this paper, we present two prediction models for predicting securities’ prices. The first model was developed using back propagation feed forward neural networks. The second model was developed using polynomial classifiers (PC), as a first time application for PC to be used in stock prices prediction. The inputs to both models were identical, and both models were trained and tested on the same data. The study was conducted on Dubai Financial Market as an emerging market and applied to two of the market’s leading stocks. In general, both models achieved very good results in terms of mean absolute error percentage. Both models show an average error around 1.5% predicting the next day price, an average error of 2.5% when predicting second day price, and an average error of 4% when predicted the third day price.

Design of Polynomial Fuzzy Neural Network Classifiers Based on Density Fuzzy C-Means and L2-Norm Regularization

In this paper, polynomial fuzzy neural network classifiers (PFNNCs) is proposed by means of density fuzzy c-means and L2-norm regularization. The overall design of PFNNCs was realized by means of fuzzy rules that come in form of three parts, namely premise part, consequence part and aggregation part. The premise part was developed by density fuzzy c-means that helps determine the apex parameters of membership functions, while the consequence part was realized by means of two types of polynomials including linear and quadratic. L2-norm regularization that can alleviate the overfitting problem was exploited to estimate the parameters of polynomials, which constructed the aggregation part. Experimental results of several data sets demonstrate that the proposed classifiers show higher classification accuracy in comparison with some other classifiers reported in the literature.

复合随机振动分析的混合PC-PEM方法

由于加工、制造等原因,实际结构系统往往所具有很多不确定性,准确评估随机系统的动力学行为不仅具有实际意义,而且是近年来结构动力学理论的一个研究热点。本文研究了同时考虑结构模型参数与所受外激励载荷具有不确定性的复合随机振动问题。结构模型参数的不确定性采用随机变量模拟,外激励载荷的不确定性采用随机过程模拟,提出了结构随机振动响应评估的混合混沌多项式-虚拟激励(PC-PEM)方法。数值算例研究了参数不确定性在21杆桁架中的传播,讨论了响应的一阶、二阶统计矩,并同蒙特卡洛方法进行对比表明提出方法的正确性和有效性。本文的工作对于考虑不确定的复杂装备与结构系统的随机振动分析具有很好的借鉴意义。

一种自适应PC-Kriging模型的结构可靠性分析方法

为提高小失效概率及耗时的复杂结构可靠性评估精度和效率,提出了一种基于PC-Kriging(polynomial-chaos-based Kriging)模型与自适应k-means聚类分析相结合的结构可靠性分析方法.PC-Kriging的回归基函数采用稀疏多项式最优截断集合来近似数值模型全局行为,并用Kriging来处理模型输出的局部变化.在基函数的建立上,PC-Kriging采用最小角回归(LAR)计算功能函数可能的多项式基函数集的数量,同时用Akaike信息准则(AIC)来确定最优多项式形式.自适应k-means聚类分析确保每次迭代添加若干个对失效概率贡献较大的样本点.通过两个数值算例分析,结果表明所提出方法在能够保证失效概率估计值的有效性和准确性的同时减小结构功能函数的评估次数.

关于Focal Loss的Tensor Train多项式分类器

在模式分类领域,多项式分类器因其复杂决策边界能力而得到广泛研究。利用TensorTrain分解形式来表示多项式分类器,可有效克服维数灾难。针对多项式分类器在训练过程中遇到的训练集分布不平衡问题,文章使用FocalLoss重塑了标准交叉损失,以降低分配给易分类样本的损失的权重,并在被广泛使用的图像分类数据集MNIST上验证了分类器的有效性。

考虑参数不确定性的列车-桥梁垂向耦合振动的PC-ARMAX代理模型研究

车-桥耦合系统不可避免的受到系统参数不确定性的影响,为了研究车-桥耦合系统参数随机性的影响,提出了可考虑动态时变系统参数不确定性的PC-ARMAX(Polynomial Chaos expansions and AutoRegressive Moving-Average with eXogenous inputs)模型。该模型采用ARMAX模型建立了不同系统参数条件下的代理模型,针对不同系统参数条件下代理模型的参数进行混沌多项式展开。在不考虑随机轨道不平顺影响的条件下,分析了车体质量、二系刚度和阻尼等参数随机性对车-桥响应的影响。研究了轨道不平顺随机性和参数不确定性共同作用的影响。结果表明:该模型的预测结果和蒙特卡洛模拟(MCS)的结果吻合,最大误差仅为2%,计算效率较MCS提高了2个~3个数量级;车体质量参数随机对车辆响应的影响最大,系统参数随机性的影响在车-桥耦合振动分析中是不可忽略,且同时考虑参数不确定性和激励随机性的影响是必要的。

PC－映照的Jacobi矩阵的快速求逆法及其应用

本文提出与多项式系数有关，从ＤＲＮ的ＰＣ－映照的Ｊａｃｏｂｉ矩阵的一种快速求逆法──逐步线性推算法，估计了它的计算工作量；进一步地，应用它导出了求算多项式全部近似复零点的一种显式迭代。最后给出若干数值试验结果。

高灰细泥对煤炭分级浮选的影响研究

文章采用两种煤样分别进行常规混合浮选和分级浮选,运用多项式拟合的方法建立了浮选精煤产率、精煤灰分、浮选完善度与药剂用量的函数关系。通过对这一数学模型的分析表明对于裕隆煤样,分级浮选可明显改善浮选效果,而对于汶上煤样,分级浮选效果不佳。通过高灰细泥与不同粒度精煤颗粒静电作用的差异对这一现象进行了解释。

基于多项式曲面拟合的TFT-LCD斑痕缺陷自动检测技术

在液晶显示器的各种视觉缺陷中,斑痕缺陷的检测是最为复杂的问题,尚未能有效解决。为了实现自动化作业,提出了一种新的斑痕缺陷检测方法。在图像分割中,提出了一种基于最小二乘法多项式曲面拟合技术的背景建模方法。利用该背景模型,可以将可能含有斑痕缺陷的图像区域从复杂背景中分割出来。在特征提取中,综合考虑了目标区域的对比度、面积、位置、轮廓、形状和亮度均匀性等特征量,建立了一种新的斑痕缺陷模型。在模式识别中,设计了基于规则的模糊分类器。该方法可以充分利用检测人员的经验和知识从而获得与人类观察者心理特点相一致的检测结果。

图象块编码—分类的方法

提出了一个基于ＤＣＴ和二维多项式近似的块分类编码算法。在该算法中，原始图象被分割成互不覆盖的８×８子块。通过依次地利用灰度局部方差、二维多项式近似误差和图象信号的空间频率分布，把图象块分为均匀、平滑、粗糙和细节４类。均匀块和平滑块分别采用零阶和一阶多项式近似。粗糙和细节块先进行ＤＣＴ变换，然后对其ＤＣＴ系数量化后采用改进的游程编码表示。实验结果表明该算法具有良好的性能。在未采用熵编码为编码码流作后处理的情况下，性能仍优于ＪＰＥＧ标准。

来流导致的高超声速气动热不确定度量化分析

通常的CFD计算都是确定性的,然而复杂工程数值模拟中必然存在误差与不确定度,分析与辨识其不确定度来源,对不确定度进行量化分析,对数值模拟可信度评估有重要意义。在高超声速飞行器气动热计算中,为获得更加可靠的气动热数据和鉴定影响气动热预测的关键因素,对返回舱开展了气动热不确定度量化分析和敏感性分析。首先选取来流速度、来流温度、壁面温度和来流密度4个不确定性输入变量,并且假定来流速度变化范围为±120 m/s(±2%),来流温度、壁面温度和来流密度变化范围为±10%。然后采用拉丁超立方抽样法生成样本,再通过热化学非平衡数值模拟方法进行气动热计算,最后分别运用基于非嵌入式多项式混沌(NIPC)的方法和基于Sobol指数的方法开展不确定度量化和敏感性分析。结果表明,在给定的输入变量不确定度的条件下,壁面热流不确定度不小于15.9%,在驻点和肩部存在峰值分别约为19.8%(0.087 MW/m^2)和17.3%(0.076 MW/m^2);相比而言,在给定变化范围内壁面热流对来流密度和来流速度更为敏感,来流温度和壁面温度对热流变化不产生明显影响。

一种基于NAPS核函数支持矢量机的说话人识别算法

介绍了指数展开分类器,引出了NAPS 核函数及核映射的概念. 详细讨论了如何利用基于NAPS 核函数的支持矢量机进行说话人识别的算法. 理论和实验表明,算法具有模型参数小、识别速度快和识别率较高的优点.

基于多项式映射的分类器及其在变压器故障诊断中的应用研究

变压器故障诊断和维修是一项复杂的任务,尽快诊断出故障并确定故障类型为即时安排相应的专业维修技术人员争取时间抢修,对于电力系统可靠供电,尤其是不允许断电的用电场所,具有非常重要的意义。文中采用多项式映射,将样本数据映射至高维空间,对高维空间的生成样本设计分类器进行分层分类。对于在线诊断系统,针对传感器或光谱仪收集数据存在较大误差的问题,文中对方法进行了"测不准"鲁棒性分析。文末给出了基于溶解氧含量(DGA)实例,并与相关研究结果进行了比较,证明文中所提出的方法的有效性和较好的鲁棒稳定性。

基于多项式拟合的电容式传感器手势识别系统

手势识别在人机交互方面具有重要的应用价值,但是传统设计方案中对用户的手势信息采集繁琐且设备昂贵,因此设计了一款基于多项式拟合的电容式传感器手势识别系统。系统采用FDC2214传感器读取数据代入到拟合好的多项式函数中,进而判别不同的手势,并利用无线通信模块将判别结果发送给PC监测终端。介绍了该系统的软硬件及算法设计,并给出了实验测试结果,结果表明:结合该算法的手势识别系统手势学习总时间为1.09 s,每个手势识别时间为0.62 s,准确率为97.41%,具有灵敏度高、准确性好,抗干扰性强的优点。

彩色图像显示系统的色彩还原技术研究

图像从输入设备到显示器观察,再到输出设备或最终的图像文件的流程中,由于不同设备的呈色机理、呈色特性、所采用的呈色空间不同,造成颜色信息在不同的设备间传递时出现了偏差,要维护原始的色彩是非常困难的。借助PCS空间,通过多项式分区回归的方法进行色彩空间转换。实验结果表明,这种方法能够实现彩色图像显示系统的色彩还原且精度较高。

Global sensitivity analysis based on high-dimensional sparse surrogate construction

Surrogate models are usually used to perform global sensitivity analysis （GSA） by avoiding a large ensemble of deterministic simulations of the Monte Carlo method to provide a reliable estimate of GSA indices. However, most surrogate models such as polynomial chaos （PC） expansions suffer from the curse of dimensionality due to the high-dimensional input space. Thus, sparse surrogate models have been proposed to alleviate the curse of dimensionality. In this paper, three techniques of sparse reconstruc- tion are used to construct sparse PC expansions that are easily applicable to computing variance-based sensitivity indices （Sobol indices）. These are orthogonal matching pursuit （OMP）, spectral projected gradient for L1 minimization （SPGL1）, and Bayesian compressive sensing with Laplace priors. By computing Sobol indices for several benchmark response models including the Sobol function, the Morris function, and the Sod shock tube problem, effective implementations of high-dimensional sparse surrogate construction are exhibited for GSA.

Novel algorithms for accurate DNA base-calling

The ability to decipher the genetic code of different species would lead to significant future scientific achievements in important areas, including medicine and agriculture. The importance of DNA sequencing necessitated a need for efficient automation of identification of base sequences from traces generated by existing sequencing machines, a process referred to as DNA base-calling. In this paper, a pattern recognition technique was adopted to minimize the inaccuracy in DNA base-calling. Two new frameworks using Artificial Neural Networks and Polynomial Classifiers are proposed to model electropherogram traces belonging to Homo sapiens, Saccharomyces mikatae and Drosophila melanogaster. De-correlation, de-convolution and normalization were implemented as part of the pre-processing stage employed to minimize data imperfections attributed to the nature of the chemical reactions involved in DNA sequencing. Discriminative features that characterize each chromatogram trace were subsequently extracted and subjected to the chosen classifiers to categorize the events to their respective base classes. The models are trained such that they are not restricted to a specific species or to a specific chemical procedure of sequencing. The base- calling accuracy achieved is compared with the exist- ing standards, PHRED (Phil’s Read Editor) and ABI (Applied Biosystems, version2.1.1) KB base-callers in terms of deletion, insertion and substitution errors. Experimental evidence indicates that the proposed models achieve a higher base-calling accuracy when compared to PHRED and a comparable performance when compared to ABI. The results obtained demon- strate the potential of the proposed models for efficient and accurate DNA base-calling.

Group Method of Data Handling for Modeling Magnetorheological Dampers

This paper proposes the use of Group Method of Data Handling (GMDH) technique for modeling Magneto-Rheological (MR) dampers in the context of system identification. GMDH is a multilayer network of quadratic neurons that offers an effective solution to modeling non-linear systems. As such, we propose the use of GMDH to approximate the forward and inverse dynamic behaviors of MR dampers. We also introduce two enhanced GMDH-based solutions. Firstly, a two-tier architecture is proposed whereby an enhanced GMD model is generated by the aid of a feedback scheme. Secondly, stepwise regression is used as a feature selection method prior to GMDH modeling. The proposed enhancements to GMDH are found to offer improved prediction results in terms of reducing the root-mean-squared error by around 40%.

基于彩色图像显示系统的色彩还原技术研究

针对由于不同设备的颜色呈现机理与空间不同,彩色图像在不同设备之间传递会产生色彩误差的问题,提出了一种基于彩色图像显示系统的色彩还原技术。该技术以多项式回归算法为基础,借助中转色彩空间(PCS),通过多项式分区回归校正算法对色彩进行空间转换,使彩色图像从输入设备到显示器、输出设备等不同设备之间传递时维护原始色彩,最后对该技术进行实验验证。

A one-dimensional polynomial chaos method in CFD–Based uncertainty quantification for ship hydrodynamic performance

A one-dimensional non-intrusive Polynomial Chaos （PC） method is applied in Uncertainty Quantification （UQ） studies for CFD-based ship performances simulations. The uncertainty properties of Expected Value （EV） and Standard Deviation （SD） are evaluated by solving the PC coefficients from a linear system of algebraic equations. The one-dimensional PC with the Legendre polynomials is applied to： （1） stochastic input domain and （2） Cumulative Distribution Function （CDF） image domain, allowing for more flexibility. The PC method is validated with the Monte-Carlo benchmark results in several high-fidelity, CFD-based, ship UQ problems, evaluating the geometrical, operational and environmental uncertainties for the Delft Catamaran 372. Convergence is studied versus PC order P for both EV and SD, showing that high order PC is not necessary for present applications. Comparison is carried out for PC with/without the least square minimization when solving the PC coefficients. The least square minimization, using larger number of CFD samples, is recommended for current test cases. The study shows the potentials of PC method in Robust Design Optimization （RDO） and Reliability-Based Design Optimization （RBDO） of ship hydrodynamic performances.