【摘要】：隨著社會經(jīng)濟的快速發(fā)展和科學技術的不斷進步,預測方法正發(fā)揮著它不可替代的作用,在預測過程中,它所運用的方案、建立的模型、使用的算法等關鍵方面也在日益成熟。簡單地講,預測就是由已知的信息推測出未來可能發(fā)生或者不可能發(fā)生的事情,其本質(zhì)上是一種對事物的發(fā)展和變化趨勢的必要認知與理性分析的過程。到目前為止,預測方法的種類已經(jīng)達到幾百種,并且多數(shù)方法在實踐中都得到了很好的應用。在預測過程中,由于每一種預測模型都是從不同角度去提取有用的信息,最后所采集的數(shù)據(jù)信息是完全不一樣的。在關于預測的研究上,許多學者都為此付出了很多的努力,其中Bates.J.M.和Granger.C.W.J.兩位學者的貢獻最為突出。1969年,他們在大量研究分析每一個單項預測模型特性的基礎上,提出了以某種合適的準則將這些模型合理有效的結(jié)合在一起的組合預測模型概念。換句話說,當一個模型的預測誤差很大時,我們不能將其舍棄,而是提取這個模型的系統(tǒng)獨立信息,并進行分析。通過多方面研究可以發(fā)現(xiàn),現(xiàn)有的傳統(tǒng)組合預測模型存在著一些不足,已經(jīng)無法滿足社會需求。由于各單項預測模型是不相同的,它采用的方法是選取適當?shù)募訖嗥骄禂?shù),使得這些模型按照設定好的配置方式進行組合,很顯然,有時候這樣的做法是不符合現(xiàn)實要求的。在整個預測模型中,如何求解加權平均系數(shù)是重中之重,論文旨在選取正確的方法進行求解,使得整個預測模型的預測精度得到顯著提高。本文首先對課題背景及意義進行調(diào)查研究,了解了問題的產(chǎn)生并找到解決方案。其次,詳細了解了相關預測技術理論,系統(tǒng)學習并掌握了確定組合預測模型權重的方式方法。隨后,闡述了基于誤差指標的線性和非線性組合預測模型,選取多個合適的實際案例分別進行仿真實驗,進行了對比分析。并引入人工蜂群算法來確定最優(yōu)組合預測模型的權重,其目的是解決確定權重時工作量大和無法保證所求權重恒大于零的問題。最后,通過引入IOWHA算子,很好地解決了傳統(tǒng)最優(yōu)不變權組合預測模型所存在的問題。以此為基礎,闡述了二階預測有效度和幾何距離這兩種概念,并嘗試與IOWHA算子結(jié)合,組成了兩種新型模型,并分別對其進行研究與說明,最后,結(jié)合實際案例,進行詳細分析。在預測的實際運用中,我們所面臨的預測對象很有可能是一個極為復雜的系統(tǒng),所建立的模型具有很強的不確定性,會大大增加預測的風險。通過多個實例分析表明,本文所建立的組合預測模型是一個性能優(yōu)良的模型,它不僅克服了傳統(tǒng)組合預測模型的缺陷,而且提高了預測精度,能夠很好的應用于實際。
[Abstract]:With the rapid development of social economy and the continuous progress of science and technology, the forecasting method is playing an irreplaceable role. In the process of prediction, the key aspects such as the scheme, the model and the algorithm used are becoming more and more mature. In short, prediction is a process of cognitive and rational analysis of the development and changing trend of things, which is to speculate from known information that may or may not happen in the future. Up to now, there are several hundred kinds of prediction methods, and most of them have been well applied in practice. In the process of prediction, because each prediction model extracts useful information from different angles, the data information collected is completely different. Many scholars, including Bates.J.M., have made a lot of efforts in the research of forecasting. And Granger.C.W.J. In 1969, on the basis of a large number of studies and analysis of the characteristics of each individual prediction model, they put forward the concept of combining these models reasonably and effectively with some appropriate criteria. In other words, when the prediction error of a model is very large, we can't abandon it, but we can extract the independent information of the model and analyze it. It can be found that the existing traditional combinatorial forecasting model has some shortcomings and can not meet the needs of the society. Because the individual prediction models are different, its method is to select the appropriate weighted average coefficient, so that these models are combined according to the set configuration mode. Obviously, sometimes this method does not meet the practical requirements. In the whole prediction model, how to solve the weighted average coefficient is the most important. This paper aims to select the correct method to solve the problem, so that the prediction accuracy of the whole prediction model can be improved significantly. In this paper, the background and significance of the subject are investigated, and solutions are found. Secondly, the related theory of prediction technology is understood in detail, and the method of determining the weight of combined prediction model is systematically studied and mastered. Then, the linear and nonlinear combined prediction models based on error index are described, and several suitable practical cases are selected to carry out the simulation experiments, and the comparative analysis is carried out. Artificial bee colony algorithm is introduced to determine the weight of the optimal combination prediction model. The purpose of the algorithm is to solve the problem that the weight can not be guaranteed to be equal to zero. Finally, by introducing IOWHA operator, the problem of traditional optimal invariant weight combination prediction model is solved well. On this basis, two concepts of second-order predictive effectiveness and geometric distance are expounded, and two new models are constructed by combining with IOWHA operator. Finally, combined with practical cases, two new models are analyzed in detail. In the practical application of prediction, the prediction object we are facing is probably a very complicated system, and the model established has strong uncertainty, which will greatly increase the risk of prediction. Through the analysis of many examples, it is shown that the combined prediction model established in this paper is a model with good performance. It not only overcomes the defects of the traditional combined prediction model, but also improves the prediction accuracy and can be applied to practice.
【學位授予單位】：山東大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP18

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