Improving the integration of piece wise linear representation and weighted support vector machine for stock trading signal prediction

The integrated piecewise linear representation (PLR) and weighted support vector machine (PLR-WSVM) has shown success in the prediction of stock trading signals. Meanwhile drawbacks of PLR-WSVM exist particularly in a real world setting. For example, the profitability using PLR-WSVM is unstable, it is not reasonable to specify same threshold value for all stocks in PLR, and critical errors in trading signals may significantly reduce the profit. In this paper, we conduct a set of improvements to PLR-WSVM. First, most of absolute technical indicators in input variables are substituted with relative indicators since the relative indicators are generally more helpful in predicting trading signals. Second, a four-class prediction problem is converted into a two-class problem in which one class is a turning point (TP) and the other is an ordinary point. And prior domain knowledge is exploited to identify either buying or selling signals from TPs. Thirdly, a delay-one-day strategy (DODS) is proposed to further correct the predicted trading signals. DODS reduces the critical errors occurring to PLR-WSVM. Finally, a procedure for selecting a threshold in PLR is provided. The threshold is automatically selected by a given percentage of TPs in a training set. The percentage of TPs is easier to understand by investor compared with the threshold. We conduct experimental study over 20 stocks, and the results confirm the expected performance of the improved PLR-WSVM. More importantly, the improved PLR-WSVM provides steady profits in average over the stocks of interest with accepted retracements.     

基于弧度距离的时间序列相似度量

 时间序列的近似表示和相似度量是时间序列数据挖掘的重要任务之一,是进行相似匹配的关键。该文针对现有的各种基于分段线性表示(Piecewise Linear Representation,PLR)相似度量方法存在的序列长度依赖和多分辨率条件下的潜在识别误差等缺点,提出了一种序列分段线性弧度表示和基于弧度距离的相似度量方法,实现了序列的快速在线分割和相似度计算。该方法简洁直观,利用分段弧度对分段趋势进行细粒度划分来保留序列主要形态特征,有效地提高了度量结果的准确性和多分辨率条件下的稳定性。该方法具有序列分割算法独立性特点,可用于时间序列的相似查询、模式匹配、分类和聚类。     

过程数据的分段线性表示

在时间序列分段线性表示(PLR)基础上，提出用相对点平均误差度量子序列过程状态变化程度，改进过程数据PLR模型分段算法，克服采用单一误差算法的模型失配问题，更加准确地反应过程状态的变化。     

两种部分负荷比PLR的定义辨析

对于工程上应用的部分负荷比（PLR）的定义和美国ASHRAE的定义的不同,利用变频多联空调的试验数据作了对比分析。结果表明,两种PLR定义值的误差在7％以内,使用工程定义的PLR,可以满足工程应用的精度要求。同时,通过比较在不同室内外空气参数条件下PLR与性能系数COP的关系,证明它们之间不存在确定的关系,指出研究两者的关系应该在确定的室内外工况下进行。     

高压电容器充电电源谐振变换器的定频控制

为有效控制高压电容器高频恒流充电电源谐振变换电路的开关频率,研制了定频控制(占空比为50%,开关频率在整个充电过程中保持不变)的20 kW高压电容器充电装置逆变电路开关电路。通过提出的充电电源电路的并联负载谐振(PLR)DC-DC变换电路的等效电路模型,研究了充电电源装置的恒流充电原理,找出了电容充电初始阶段谐振电流和开关频率的数值关系。实验研究结果表明,当谐振变换电路开关频率接近于等效电路固有谐振频率的奇数分之一时,产生较大的谐振电流;为了实现谐振变换电路开关器件的零电流开通和关断,开关频率的大小始终可控制在小于等效电路固有谐振频率的1/2的范围之内。     

VANET无线链路质量的测量与分析

目前对移动自组织网络的研究很少涉及现实环境里的测量问题。该文对不同环境下无线链路丢包率、信号强度等参数进行了测量和分析。结果表明,环境对链路质量有较大影响,在郊区环境下,丢包率较低,链路质量较好,信号强度反应链路质量的能力有限,连续丢包概率的变化与丢包率平均值的变化基本一致。分析结果对预测无线链路质量和设计VANET网络协议具有重要意义。     

基于纠删码的海洋无线传感器网络丢包恢复方法

针对无线传感器网络在恶劣海洋环境下存在的严重丢包问题,设计并实现了一种低复杂度的基于里德-所罗门(RS)码 的丢包恢复方法。 具体而言,发送节点对信息数据包进行分析,进一步利用 RS 码编码生成并插入少量的冗余数据包,可以保 证数据包的顺序发送,同时利用较少的资源编码生成了冗余校验数据包。 接收节点基于接收的信息数据包与校验数据包,通过 RS 码对丢失的数据包进行纠删恢复。 在陆地和海洋两种复杂信道中测试提出的方法,以丢包率(packet loss rate, PLR)作为 RS 码丢包恢复方法性能的度量指标。 测试结果表明,采用低复杂度的 RS 码丢包恢复方法能降低信道衰落影响的丢包率,保证了 数据在严重丢包情况下的完整性,提高系统的可靠性。     

慢光缓存器及其在光分组交换中的应用

将一种新的光缓存技术--慢光可变延时缓存器,与可调波长转换器结合,构成光分组交换模型.简要分析了慢光缓存器的时延特性,并对其解决信息网络交换拥塞进行了应用设计与仿真,分析结果表明,慢光缓存器与光纤延迟线缓存相比较,不仅能更有效地降低丢包率,而且还可以简化光分组交换的结构,其体积更小,使用更灵活.     

多媒体流在网络中的同步算法

为了解决多媒体包的流在传输过程中不同步问题，设计并提出了一种称为一致性的新算法，为单个和多个流的同步化问题提供了一个集成解决方案。它定义了一个框架，既可用以解决同步化问题，又可以利用相关应用程序参数进行操作。这些参数可以在丢包率、端到端延迟和流不对称性之间作出折衷。进一步讨论了如何处理多个流同步的问题、计算接收端缓冲空间和网络延迟的问题。     

Simple Plots Improve Software Reliability Prediction Models

Software reliability prediction is accomplished by fitting a nonhomogeneous Poisson process (NHPP) model to data from software testing. The data consist of the cumulative time and the cumulative number of failures found in software testing. The NHPP model can be used to predict the reliability of the software product at the time of release or to determine how much further testing must be done to reach a specified failure rate. Models are normally fitted to software testing data using Poisson regression by the method of maximum likelihood. We encountered difficulties fitting models when numerical algorithms failed to converge or when we were unable to discriminate among several models with the same number of parameters. These difficulties were the result of having no likelihood ratio test to compare models with the same number of parameters and anomalies in the data that caused numerical algorithms to fail. We found that a simple cumulative plot of the data (cumulative failures on the vertical axis vs. cumulative test time on the horizontal axis) helped in spotting anomalies in the data and selecting an appropriate model to fit. A second plot of running products of ratios of the probability densities for the predictions made from competing models, called the prequential likelihood ratio, helped in discriminating between models. Use of these plots helped resolve the difficulties we experienced in fitting models to the software testing data.