【摘要】：作為干散貨航運(yùn)市場(chǎng)的“晴雨表”,干散貨運(yùn)價(jià)指數(shù)反映了干散貨運(yùn)輸市場(chǎng)的運(yùn)價(jià)水平。由于受到多種因素的影響,近年來(lái)干散貨運(yùn)價(jià)指數(shù)始終處于劇烈波動(dòng)之中,且走勢(shì)難以琢磨,表現(xiàn)出了復(fù)雜的非線性特征,傳統(tǒng)的預(yù)測(cè)方法難以取得良好的預(yù)測(cè)效果,這也給干散貨航運(yùn)市場(chǎng)經(jīng)營(yíng)者的決策帶來(lái)了困難。 干散貨運(yùn)價(jià)指數(shù)波動(dòng)劇烈,蘊(yùn)含了國(guó)際干散貨航運(yùn)市場(chǎng)長(zhǎng)期以來(lái)的演化信息。本文在深刻分析干散貨運(yùn)價(jià)指數(shù)波動(dòng)的內(nèi)在規(guī)律及外在影響的基礎(chǔ)上,提出結(jié)合混沌時(shí)間序列分析和支持向量機(jī)(Support Vector Machine, SVM)回歸原理的混合預(yù)測(cè)模型,對(duì)干散貨運(yùn)價(jià)指數(shù)(Baltic Dry Index, BDI)進(jìn)行了有效地預(yù)測(cè)。 本文首先對(duì)國(guó)際干散貨航運(yùn)的供需市場(chǎng)進(jìn)行深入分析,揭示了干散貨市場(chǎng)運(yùn)價(jià)波動(dòng)的內(nèi)在原因。其次,文中闡述了干散貨運(yùn)價(jià)指數(shù)的成因及航線構(gòu)成,并對(duì)運(yùn)價(jià)指數(shù)的影響因素及波動(dòng)性進(jìn)行了定性分析,為選擇適當(dāng)?shù)念A(yù)測(cè)方法奠定了基礎(chǔ)。鑒于干散貨運(yùn)價(jià)指數(shù)的非線性特征,本文提出了結(jié)合混沌時(shí)間序列分析的相空間重構(gòu)和支持向量機(jī)(SVM)的混合預(yù)測(cè)模型,探討并闡述了混合模型的預(yù)測(cè)原理及建模思路。接著,本文在對(duì)混合預(yù)測(cè)模型關(guān)鍵參數(shù)的選取進(jìn)行系統(tǒng)分析的基礎(chǔ)上,建立了參數(shù)聯(lián)合優(yōu)化問(wèn)題的數(shù)學(xué)模型,并采用遺傳算法對(duì)該優(yōu)化問(wèn)題進(jìn)行求解。最后,選取BDl月度均值進(jìn)行實(shí)證分析,對(duì)BDI樣本序列進(jìn)行混沌性識(shí)別,驗(yàn)證混合預(yù)測(cè)模型的可行性；對(duì)樣本序列進(jìn)行噪聲平滑等處理,通過(guò)構(gòu)建混合預(yù)測(cè)模型對(duì)數(shù)據(jù)處理后的BDI序列進(jìn)行單步和多步預(yù)測(cè),在單步預(yù)測(cè)中分別采用傳統(tǒng)的單獨(dú)參數(shù)優(yōu)化方法與基于遺傳算法的參數(shù)聯(lián)合優(yōu)化進(jìn)行仿真實(shí)驗(yàn),采用遺傳算法進(jìn)行參數(shù)的優(yōu)化選取,提高了SVM混合模型的預(yù)測(cè)能力。通過(guò)與ARIMA模型和神經(jīng)網(wǎng)絡(luò)模型進(jìn)行比較,預(yù)測(cè)結(jié)果分析表明,SVM混合模型子啊BDI序列的單步和多步預(yù)測(cè)中具有較高的預(yù)測(cè)精度,能夠更好地把握運(yùn)價(jià)指數(shù)的變化趨勢(shì)。
[Abstract]:As a barometer of dry bulk shipping market, the index of dry bulk freight rate reflects the level of freight rate in dry bulk transportation market. Due to the influence of many factors, the dry bulk freight rate index has been fluctuating sharply in recent years, and the trend is difficult to figure out, showing complex nonlinear characteristics, so it is difficult for the traditional forecasting methods to obtain good prediction results. This has also given dry bulk shipping market operators decision-making difficulties. The price index of dry bulk goods fluctuates sharply, which contains the evolution information of international dry bulk shipping market for a long time. On the basis of deep analysis of the inherent law and external influence of the fluctuation of dry bulk freight rate index, a hybrid forecasting model combining chaotic time series analysis and (Support Vector Machine, SVM) regression principle of support vector machine is proposed in this paper. The dry bulk freight rate index (Baltic Dry Index, BDI) is effectively forecasted. This paper first analyzes the supply and demand market of international dry bulk shipping and reveals the internal reasons of the fluctuation of freight rate in dry bulk shipping market. Secondly, the cause of formation and route composition of dry bulk freight rate index are expounded, and the influencing factors and fluctuation of freight rate index are qualitatively analyzed, which lays a foundation for choosing appropriate forecasting methods. In view of the nonlinear characteristics of dry bulk freight rate index, this paper presents a phase space reconstruction model combined with chaotic time series analysis and a hybrid prediction model based on support vector machine (SVM). The prediction principle and modeling idea of the hybrid model are discussed and expounded. Then, based on the systematic analysis of the selection of the key parameters of the hybrid prediction model, the mathematical model of the joint parameter optimization problem is established, and the genetic algorithm is used to solve the optimization problem. Finally, the BDl monthly mean is selected for empirical analysis to identify chaos in the BDI sample sequence to verify the feasibility of the hybrid prediction model. The sample sequence is processed by noise smoothing, and the BDI sequence after data processing is predicted by constructing a mixed prediction model. The traditional single parameter optimization method and the parameter optimization based on genetic algorithm are used to simulate the single step prediction, and the genetic algorithm is used to optimize and select the parameters. The prediction ability of SVM hybrid model is improved. Compared with the ARIMA model and the neural network model, the prediction results show that the single-step and multi-step prediction of the BDI sequence with the SVM mixed model has higher prediction accuracy and can better grasp the variation trend of the freight rate index.
【學(xué)位授予單位】：大連海事大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2013
【分類號(hào)】：U695.27;F551;F224

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 曾慶成;神經(jīng)網(wǎng)絡(luò)在波羅的海運(yùn)價(jià)指數(shù)預(yù)測(cè)中的應(yīng)用研究[J];大連海事大學(xué)學(xué)報(bào);2004年03期

2 董良才;黃有方;胡顥;;基于模糊神經(jīng)網(wǎng)絡(luò)的航運(yùn)運(yùn)價(jià)指數(shù)預(yù)測(cè)[J];大連海事大學(xué)學(xué)報(bào);2010年04期

3 宋召青;崔和;胡云安;;支持向量機(jī)理論的研究與進(jìn)展[J];海軍航空工程學(xué)院學(xué)報(bào);2008年02期

4 吳少雄;黃恩洲;;基于支持向量機(jī)的控制圖模式識(shí)別[J];計(jì)算機(jī)應(yīng)用;2007年01期

5 閻威武,邵惠鶴;支持向量機(jī)和最小二乘支持向量機(jī)的比較及應(yīng)用研究[J];控制與決策;2003年03期

6 路應(yīng)金;唐小我;張勇;;供應(yīng)鏈產(chǎn)品定價(jià)行為混沌特性及其混沌預(yù)測(cè)[J];控制與決策;2006年04期

7 趙倩;胡越黎;曹家麟;;基于支持向量機(jī)和遺傳算法的皮膚顯微圖像特征選擇[J];模式識(shí)別與人工智能;2005年04期

8 李正宏,袁紹宏;波羅的海運(yùn)價(jià)指數(shù)相關(guān)性分析[J];水運(yùn)管理;2004年08期

9 李萬(wàn)慶;李海濤;孟文清;;基于支持向量機(jī)的降水量混沌時(shí)間序列預(yù)測(cè)[J];統(tǒng)計(jì)與決策;2007年19期

10 譚文,王耀南,周少武,劉祖潤(rùn);混沌時(shí)間序列的模糊神經(jīng)網(wǎng)絡(luò)預(yù)測(cè)[J];物理學(xué)報(bào);2003年04期

相關(guān)碩士學(xué)位論文 前9條

1 盧興林;基于GARCH模型的國(guó)際干散貨運(yùn)價(jià)指數(shù)波動(dòng)性研究[D];大連海事大學(xué);2010年

2 趙海洋;FFA在干散貨航運(yùn)市場(chǎng)規(guī)避風(fēng)險(xiǎn)的應(yīng)用研究[D];大連海事大學(xué);2010年

3 夏天俊;基于自適應(yīng)神經(jīng)網(wǎng)絡(luò)的BDI預(yù)測(cè)研究[D];大連海事大學(xué);2011年

4 張舜;國(guó)際干散貨航運(yùn)市場(chǎng)供需平衡分析[D];大連海事大學(xué);2012年

5 王君;基于神經(jīng)網(wǎng)絡(luò)的混沌時(shí)間序列預(yù)測(cè)[D];西南交通大學(xué);2009年

6 范群林;石油期貨價(jià)格混沌時(shí)間序列預(yù)測(cè)方法研究[D];沈陽(yáng)大學(xué);2008年

7 靳廉潔;基于支持向量機(jī)的干散貨運(yùn)價(jià)指數(shù)預(yù)測(cè)研究[D];大連海事大學(xué);2010年

8 趙春曉;基于支持向量機(jī)的混沌時(shí)間序列預(yù)測(cè)方法的研究[D];東北大學(xué);2008年

9 劉金霞;干散貨航運(yùn)市場(chǎng)間運(yùn)價(jià)指數(shù)波動(dòng)溢出效應(yīng)研究[D];大連海事大學(xué);2012年

，

本文編號(hào)：2348196