【摘要】：作為干散貨航運市場的“晴雨表”,干散貨運價指數(shù)反映了干散貨運輸市場的運價水平。由于受到多種因素的影響,近年來干散貨運價指數(shù)始終處于劇烈波動之中,且走勢難以琢磨,表現(xiàn)出了復雜的非線性特征,傳統(tǒng)的預測方法難以取得良好的預測效果,這也給干散貨航運市場經(jīng)營者的決策帶來了困難。 干散貨運價指數(shù)波動劇烈,蘊含了國際干散貨航運市場長期以來的演化信息。本文在深刻分析干散貨運價指數(shù)波動的內在規(guī)律及外在影響的基礎上,提出結合混沌時間序列分析和支持向量機(Support Vector Machine, SVM)回歸原理的混合預測模型,對干散貨運價指數(shù)(Baltic Dry Index, BDI)進行了有效地預測。 本文首先對國際干散貨航運的供需市場進行深入分析,揭示了干散貨市場運價波動的內在原因。其次,文中闡述了干散貨運價指數(shù)的成因及航線構成,并對運價指數(shù)的影響因素及波動性進行了定性分析,為選擇適當?shù)念A測方法奠定了基礎。鑒于干散貨運價指數(shù)的非線性特征,本文提出了結合混沌時間序列分析的相空間重構和支持向量機(SVM)的混合預測模型,探討并闡述了混合模型的預測原理及建模思路。接著,本文在對混合預測模型關鍵參數(shù)的選取進行系統(tǒng)分析的基礎上,建立了參數(shù)聯(lián)合優(yōu)化問題的數(shù)學模型,并采用遺傳算法對該優(yōu)化問題進行求解。最后,選取BDl月度均值進行實證分析,對BDI樣本序列進行混沌性識別,驗證混合預測模型的可行性；對樣本序列進行噪聲平滑等處理,通過構建混合預測模型對數(shù)據(jù)處理后的BDI序列進行單步和多步預測,在單步預測中分別采用傳統(tǒng)的單獨參數(shù)優(yōu)化方法與基于遺傳算法的參數(shù)聯(lián)合優(yōu)化進行仿真實驗,采用遺傳算法進行參數(shù)的優(yōu)化選取,提高了SVM混合模型的預測能力。通過與ARIMA模型和神經(jīng)網(wǎng)絡模型進行比較,預測結果分析表明,SVM混合模型子啊BDI序列的單步和多步預測中具有較高的預測精度,能夠更好地把握運價指數(shù)的變化趨勢。
[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.
【學位授予單位】：大連海事大學
【學位級別】：碩士
【學位授予年份】：2013
【分類號】：U695.27;F551;F224

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