本文選題：時間序列 + 最小最大概率回歸機　； 參考：《蘭州交通大學》2014年碩士論文

【摘要】：時間序列預測伴隨時代的進步日益重要，應用研究領(lǐng)域無處不在，常見的例如經(jīng)濟預測、天氣預測、交通流預測和網(wǎng)絡流量預測等。智能交通系統(tǒng)預測研究是路網(wǎng)交通流量實時在線控制規(guī)劃的重要信息源泉�；ヂ�(lián)網(wǎng)網(wǎng)絡流量實時幀數(shù)據(jù)合理分配對網(wǎng)絡擁塞的緩解和網(wǎng)絡安全的管理提供了便捷幫助。 最小最大概率回歸機(Minimax Probability Machine Regression,MPMR)是一種將概率分類機器學習用于解決回歸問題的新型預測方法，在掌紋識別、圖像分割、數(shù)據(jù)挖掘、電力預測等領(lǐng)域中得到了廣泛的應用。文中結(jié)合混沌理論、遞歸圖可預測性分析，將MPMR方法用于交通流和網(wǎng)絡視頻流的單步預測和直接多步預測實驗中，通過核函數(shù)映射，在最優(yōu)核參數(shù)條件下獲取能夠最大概率容納預測點落入最小回歸管道內(nèi)的epsilon管道值，并與支持向量機(Support Vector Machine,SVM)預測方法、人工神經(jīng)網(wǎng)絡預測方法進行預測實驗比較，驗證了該方法的優(yōu)越性。 本文主要研究內(nèi)容包括以下幾個方面： (1)在貝葉斯學習的基礎(chǔ)上，研究了線性最小最大概率機分類(Minimax ProbabilityMachine,MPM)方法、非線性最小最大概率機分類(Minimax Probability MachineClassification,MPMC)方法，并將其延伸至MPMR回歸方法。 (2)針對非線性時間序列，研究了相應的混沌理論，進一步利用最大李雅普諾夫指數(shù)判別三組時間序列的混沌特性，并研究了確定最優(yōu)嵌入維數(shù)m的Cao方法，確定最優(yōu)延遲時間τ的互信息法，和判斷時間序列可預測性的遞歸圖方法。 (3)將概率學習機MPMR方法應用在Mackey-Glass混沌時間序列、短時交通流及網(wǎng)絡視頻流預測應用中，，并與現(xiàn)有同等條件下的預測方法比較實驗效果，驗證該方法的先進性和有效性。 (4)基于RBF核函數(shù)，MPMR研究了相應預測回歸管道選取不同值時對預測精度的影響，驗證了該方法的魯棒性。
[Abstract]:Time series prediction is becoming more and more important with the development of the times. The applied research fields are ubiquitous, such as economic forecasting, weather forecasting, traffic flow forecasting and network traffic forecasting. Intelligent Transportation system (its) prediction is an important source of information for real-time and on-line control planning of road network traffic flow. The reasonable allocation of real-time frame data of Internet traffic provides convenient help to alleviate network congestion and manage network security. Minimax Probability Machine regression machine (MPMRs) is a new prediction method which uses probabilistic classification machine learning to solve regression problems. It has been widely used in palmprint recognition, image segmentation, data mining, power prediction and so on. Combined with chaos theory and recursive graph predictive analysis, MPMR method is applied to single step prediction and direct multistep prediction of traffic flow and network video flow. Under the condition of optimal kernel parameters, the value of epsilon pipeline with maximum probability of accommodating the predicted point into the minimum regression pipeline is obtained, and compared with the support vector machine support Vector machine prediction method and the artificial neural network prediction method. The superiority of this method is verified. The main contents of this paper include the following aspects: 1) on the basis of Bayesian learning, the minimax probability machine classification method and the nonlinear minimum maximum probability machine classification method are studied, and the method is extended to the MPMR regression method. (2) for the nonlinear time series, the corresponding chaos theory is studied, and the chaos characteristics of the three groups of time series are judged by using the maximum Lyapunov exponent, and the Cao method for determining the optimal embedding dimension m is studied. The mutual information method for determining the optimal delay time 蟿 and the recursive graph method for judging the predictability of time series. 3) the probabilistic learning machine (MPMR) method is applied to the prediction of Mackey-Glass chaotic time series, short-term traffic flow and network video flow, and the experimental results are compared with the existing prediction methods under the same conditions. The results show that the proposed method is advanced and effective. 4) based on the RBF kernel function, the influence of different values on the prediction accuracy is studied, and the robustness of the method is verified.
【學位授予單位】：蘭州交通大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：U491.14;TP18

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