兰州大学机构库 >数学与统计学院
基于头脑风暴算法优化的v-SVR的研究及应用
Alternative TitleThe Study and Application of v-SVR Based Brain Storming Algorithm--a Case of Stock Market
沈林
Thesis Advisor王建州
2014-05-31
Degree Grantor兰州大学
Place of Conferral兰州
Degree Name硕士
Keyword支持向量机 v-SVR 头脑风暴优化算法 粒子群算法 主成分分析
Abstract智能预测因其在处理非线性系统问题上的优势而被更多的用于股票市场预测. 支持向量机作为智能预测中最成熟的理论之一倍受青睐. 现在对于标准的支持向量机研究很多, 而对改进的v-SVR的研究还不是很多. 本文利用2011年4月20日至2011年12月9日的沪深300指数,深证综合指数的数据分别建立v-SVR模型, 选取了20个一般我们认为对股票市场指数有较大影响的指标, 这20个指标主要包括开盘价、收盘价、最高价、最低价、成交量、成交金额, 涨跌幅, 涨跌额, 移动平均线MA(5)、移动平均线MA(10)、移动平均线MA(20)、异同平均数MACD、正负差DIF、随机指标KDJ.K、随机指标KDJ.D、心理线指标PSYMA(6)、相对强弱指标RSI(6)、相对强弱指标RSI(12)、乖离率指标BIAS(6)、乖离率指标BIAS(24). 基于这些数据, 试图建立次日的开盘价与这些因素之间的支持向量回归模型. 先对数据进行预处理, 预处理的方法主要是统计学中常用的标准化处理、相关性分析和主成分分析, 经过预处理的数据在建立模型时更加合理, 而且效果更好, 然后用v-SVR对数据建立回归模型, 进行预测和分析. 根据相关文献, 支持向量机的参数选择一直都是一个问题, 在选择v-SVR的3个相关参数c,g,v时, 本文提出用头脑风暴优化算法(Brain Storming Algorithm, BSO)来选取参数, 并与LIBSVM软件包默认的参数, 网格算法选取参数, 粒子群算法(Particle Swarm Optimization, PSO)选取相关参数比较, 结果头脑风暴算法选取参数在这四种方法中表现是最好的.
Other AbstractIntelligent prediction is used to predict stock market more because of its advantages in dealing with the nonlinear system problems. AS one of the most mature theory in the intelligent predictions, Support vector machine is acclaimed. Now there are a lot of research about the standard SVM, but for v-SVR is little. In this paper, the CSI 300 Index and shenzhen Composite Index are predicted. The Index from April 20, 2011 to 9 December ,2011 altogether 160 days’ data are selected for the study .Select 20 pairs Index influential factors including opening price, closing price, highest price, lowest price, trading volume, transaction amount, price change , change amount , The moving average line MA (5), the moving average line MA (10), the moving average line MA(20), Similarities and differences between the average MACD, plus or minus the difference DIF, stochastics KDJ.K, stochastics KDJ.D, psychological line indicator PSYMA (6), Relative Strength Index RSI (6), Relative Strength Index RSI (12), the deviation rate indicators BIAS (6), deviation rate index BIAS (24) . These factors are generally thought to have a bigger impact on the stock Index . Try to establish support vector regression model between the next day’s opening price and these factors. First, these date are preprocessed, the pretreatment method are mainly the standardization of statistical processing, correlation analysis and principal component analysis. The processed date are better for the modeling effect. Then the data are analyzed by v-SVR, according to the relevant literature, parameters selection of svm is always a problem. In the choice of three related parameters, this paper puts forward with brain storming optimization (BSO) algorithm to select parameters. Compared with the default parameters of LIBSVM package, parameters selected by grid algorithm and parameters selected by particle swarm optimization (PSO) algorithm, brain storming method selecting parameters is the best of the four methods.
URL查看原文
Language中文
Document Type学位论文
Identifierhttps://ir.lzu.edu.cn/handle/262010/225109
Collection数学与统计学院
Recommended Citation
GB/T 7714
沈林. 基于头脑风暴算法优化的v-SVR的研究及应用[D]. 兰州. 兰州大学,2014.
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