本文選題：支持向量機(jī)　切入點(diǎn)：時(shí)間序列預(yù)測　出處：《西南財(cái)經(jīng)大學(xué)》2013年碩士論文　論文類型：學(xué)位論文

【摘要】：支持向量機(jī)理論是近幾年發(fā)展起來,專門處理小樣本的條件下分類與預(yù)測問題的新方法,源自于機(jī)器學(xué),同時(shí)廣泛應(yīng)用于時(shí)間序列預(yù)測理論和金融市場預(yù)測等方面,能夠有效解決小樣本、非線性、高維數(shù)和局部極小值等問題。然而,標(biāo)準(zhǔn)支持向量機(jī)假設(shè)的樣本分布過于理想,處理復(fù)雜的實(shí)際問題時(shí)難免遇到困難。因此,本文以解決實(shí)際問題為線索,根據(jù)對時(shí)間序列數(shù)據(jù)預(yù)測的要求,以提高計(jì)算效率,降低優(yōu)化問題復(fù)雜度為前提,探索一些極端條件下支持向量機(jī)的新理論、新方法。 本文在文獻(xiàn)[1]的基礎(chǔ)上作了一些工作。文獻(xiàn)[1]中闡述了單變量morlet小波應(yīng)用到支持向量機(jī)理論的模型并利用了小波支持向量機(jī)方法來對金融市場數(shù)據(jù)進(jìn)行預(yù)測,與文獻(xiàn)[1]不同的是,本文中將探索基于morlet小波支持向量機(jī)與多變量預(yù)測技術(shù)相結(jié)合的方法并將其應(yīng)用到對股票指數(shù)的預(yù)測中。
[Abstract]:Support vector machine (SVM) theory is a new method developed in recent years to deal with classification and prediction problems under the condition of small samples. It originates from machine science and is widely used in time series forecasting theory and financial market forecasting. It can effectively solve the problems of small sample, nonlinear, high dimension and local minimum. However, the sample distribution of the assumption of standard support vector machine is too ideal, so it is difficult to deal with complex practical problems. Based on the clue of solving practical problems and the requirement of time series data prediction, this paper explores new theories and methods of support vector machine under some extreme conditions on the premise of improving computational efficiency and reducing the complexity of optimization problems. In this paper, some work is done on the basis of reference [1]. In reference [1], the model of applying univariate morlet wavelet to support vector machine theory is described and the wavelet support vector machine method is used to predict the financial market data. Different from the reference [1], this paper will explore the method of combining morlet wavelet support vector machine with multivariable prediction technology and apply it to the prediction of stock index.
【學(xué)位授予單位】：西南財(cái)經(jīng)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2013
【分類號】：F830.91;TP181

【參考文獻(xiàn)】

相關(guān)期刊論文 前1條

1 湯凌冰;盛煥燁;湯凌霄;;基于小波支持向量機(jī)的金融預(yù)測[J];湘潭大學(xué)自然科學(xué)學(xué)報(bào);2009年01期

，

本文編號：1629132