本文關鍵詞：基于EMD的時間序列預測混合建模技術及其應用研究　出處：《華中科技大學》2014年博士論文　論文類型：學位論文

  更多相關文章： 經(jīng)驗模態(tài)分解 端點效應 時間序列預測 多步預測 區(qū)間型時間序列

【摘要】：經(jīng)驗模態(tài)分解(Empirical Mode Decomposition, EMD)是對非平穩(wěn)信號進行時頻分析的理想工具,目前該方法在許多科學和工程領域得到了廣泛的應用。然而,EMD在非線性時間序列建模和預測中的應用研究卻相對匱乏,相關應用研究尚不深入,沒有充分展示EMD的優(yōu)越性。本文的目的是將基于EMD的混合建模框架更深入地應用到非線性時間序列分析和預測研究中,同時結合商業(yè)與金融領域中的預測問題,設計適合的預測模型與方法。 本文的主要研究內容如下： 本文首先提出一個基于集合經(jīng)驗模態(tài)分解(Ensemble Empirical Mode Decomposition, EEMD)和支持向量機(Support Vector Machines, S VM)的混合預測模型,同時結合股票價格預測這一金融市場研究中的傳統(tǒng)熱點進行實證研究。 其次,現(xiàn)有基于EMD的混合建�？蚣芗皯醚芯烤纯紤]端點效應,針對以上弊端,本文提出抑制端點效應的基于EMD和SVMs的預測建模框架,并從預測準確度的視角比較四種主流的抑制端點效應的方法。經(jīng)大量數(shù)據(jù)實驗和相關方法的比較,結果證明端點效應對基于EMD的混合建模框架的預測性能有較大的負面影響。在此基礎上,本文將斜率法引入傳統(tǒng)的基于EMD技術的混合建模框架,并運用較EMD更優(yōu)的EEMD技術對時間序列進行分解,提出抑制端點效應的基于EEMD和SVM的預測模型對具有高度波動性的航空客流進行預測。 再次,本文對基于EMD的時間序列多步預測及預測策略進行研究。(1)針對已有預測策略存在的諸多不足,本文提出一個基于粒子群優(yōu)化算法(Particle Swarm Optimization, PSO)的變預測步長多輸入多輸出預測策略(PSO-MISMO),并以人工數(shù)據(jù)和NN3競賽數(shù)據(jù)為預測對象,多種策略為對比方法,從預測準確度、收斂性和訓練耗時等方面評估PSO-MISMO策略的實用性。(2)針對現(xiàn)有基于EMD技術的建�？蚣芫窒抻趩尾筋A測應用的情形,本文提出適用于時間序列多步預測的基于EMD技術的建模框架,同時結合國際原油價格預測這一能源經(jīng)濟研究中的傳統(tǒng)熱點進行實證研究。(3)傳統(tǒng)支持向量回歸算法因其單輸出結構的特征僅能采用迭代或直接策略,而不能采用較以上兩種策略更優(yōu)的MIMO策略進行時間序列多步預測。針對以上弊端,本文提出適用于MIMO策略的多輸出支持向量回歸算法對時間序列進行多步預測。 最后,針對現(xiàn)有研究局限于單值預測的情形,本文從兩個不同的研究視角分別提出適用于區(qū)間型時間序列預測的方法。(1)在保留區(qū)間型數(shù)據(jù)特征的情形下,憑借多輸出支持向量回歸算法(Multiple-output Support Vector Regression, MSVR)的多輸出結構特征和螢火蟲算法(Firefly Algorithm, FA)的高效優(yōu)化能力,本文提出適用于區(qū)間型時間序列預測的基于MSVR和FA的混合模型,同時結合區(qū)間型股票價格指數(shù)預測這一金融市場研究中的新興熱點進行實證研究。(2)在不保留區(qū)間型數(shù)據(jù)特征情形下,憑借雙變量經(jīng)驗模態(tài)分解技術(Bivariate Empirical Mode Decomposition, BEMD)對復值序列高效的分解性能和基于EMD技術的建模框架在單值時間序列預測中的優(yōu)異表現(xiàn),本文提出基于BEMD技術的建模框架對區(qū)間型時間序列進行預測,同時結合區(qū)間型電力需求預測這一電力市場研究中的新興熱點進行實證研究。
[Abstract]:The empirical mode decomposition (Empirical Mode Decomposition, EMD) is an ideal tool for time-frequency analysis of non-stationary signal, this method has been widely used in many fields of science and engineering. However, the research and application of EMD in nonlinear time series modeling and forecasting is relatively scarce, relevant applied research is not deep, not fully demonstrate the superiority of EMD. The purpose of this paper is the hybrid modeling framework EMD further applied to nonlinear time series analysis and prediction research based on combined prediction of business and the financial sector in question, prediction model and the method of design for.
The main contents of this paper are as follows:
This paper proposes a based on ensemble empirical mode decomposition (Ensemble Empirical Mode Decomposition, EEMD) and support vector machine (Support Vector Machines, S VM) of the hybrid forecasting model, combined with the prediction of stock price of traditional hot point of this financial market research in empirical research.
Secondly, the existing hybrid modeling framework and Application Research Based on EMD are not considering end effect, in view of the above problems, this paper puts forward based on predictive modeling framework of EMD and SVMs to suppress the endpoint effect, and from the suppression of the endpoint effect prediction accuracy from the perspective of comparison of four mainstream methods. By comparison of large amounts of experimental data and related methods the results prove that the endpoint effect, there is a greater negative impact on the prediction performance of hybrid modeling framework based on EMD. On this basis, this paper will be based on the hybrid modeling framework of EMD technology into the traditional slope method, and the use of better than EMD EEMD technology for time series decomposition is proposed to suppress the endpoint effect prediction model and EEMD based on the SVM of air passenger flow with a high degree of volatility forecasting.
Again, this paper focuses on the research of EMD time series prediction and forecast based on strategy. (1) aiming at existing problems of prediction strategy, this paper proposes an algorithm based on particle swarm optimization (Particle Swarm Optimization, PSO) of the variable prediction step, multiple input multiple output (PSO-MISMO), and the prediction strategy based on artificial the data and NN3 data for the prediction of competition strategy for a variety of objects, the method of comparison, from the forecast accuracy, evaluate the usefulness of PSO-MISMO strategy and convergence time. Training (2) according to the existing modeling framework of EMD technology is limited to single step prediction application case based on the proposed modeling framework based on EMD technology for time the sequence of multi step prediction, combined with the international crude oil price prediction of traditional hot this energy in economic research and empirical research. (3) the traditional support vector regression algorithm for single output structure The feature can only adopt iterative or direct strategy, and can't use more than two strategies and better MIMO strategy for multi-step prediction of time series. In view of the above drawbacks, this paper proposes a multi output support vector regression algorithm suitable for MIMO strategy for multi-step prediction of time series.
Finally, according to the existing research is limited to single value prediction, this paper from two different perspectives respectively put forward methods applied to interval time series prediction. (1) in the retention interval data characteristics of the case, with multi output support vector regression algorithm (Multiple-output Support Vector Regression, MSVR) multi output the structure and characteristics of firefly algorithm (Firefly Algorithm FA), the ability of optimization, this paper applies a hybrid model of MSVR and FA based on interval time series prediction, combined with the interval of stock price index forecasting emerging hot spots in this financial market research in empirical research. (2) in the retention interval the data type feature case with double variable EMD Technology (Bivariate Empirical Mode Decomposition, BEMD) of complex valued sequence efficient decomposition properties and based on EMD Technology The performance of modeling framework in single value time series prediction is excellent. In this paper, a modeling framework based on BEMD technology is proposed to predict interval type time series, and combined with the emerging hot spots in the research of electricity market, the empirical research is carried out based on interval power demand forecasting.

【學位授予單位】：華中科技大學
【學位級別】：博士
【學位授予年份】：2014
【分類號】：F830.91;F224

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