【摘要】：隨著社會(huì)經(jīng)濟(jì)的快速發(fā)展和科學(xué)技術(shù)的不斷進(jìn)步,預(yù)測(cè)方法正發(fā)揮著它不可替代的作用,在預(yù)測(cè)過(guò)程中,它所運(yùn)用的方案、建立的模型、使用的算法等關(guān)鍵方面也在日益成熟。簡(jiǎn)單地講,預(yù)測(cè)就是由已知的信息推測(cè)出未來(lái)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(duì)事物的發(fā)展和變化趨勢(shì)的必要認(rèn)知與理性分析的過(guò)程。到目前為止,預(yù)測(cè)方法的種類(lèi)已經(jīng)達(dá)到幾百種,并且多數(shù)方法在實(shí)踐中都得到了很好的應(yīng)用。在預(yù)測(cè)過(guò)程中,由于每一種預(yù)測(cè)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(guān)于預(yù)測(cè)的研究上,許多學(xué)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(xué)者的貢獻(xiàn)最為突出。1969年,他們?cè)诖罅垦芯糠治雒恳粋�(gè)單項(xiàng)預(yù)測(cè)模型特性的基礎(chǔ)上,提出了以某種合適的準(zhǔn)則將這些模型合理有效的結(jié)合在一起的組合預(yù)測(cè)模型概念。換句話(huà)說(shuō),當(dāng)一個(gè)模型的預(yù)測(cè)誤差很大時(shí),我們不能將其舍棄,而是提取這個(gè)模型的系統(tǒng)獨(dú)立信息,并進(jìn)行分析。通過(guò)多方面研究可以發(fā)現(xiàn),現(xiàn)有的傳統(tǒng)組合預(yù)測(cè)模型存在著一些不足,已經(jīng)無(wú)法滿(mǎn)足社會(huì)需求。由于各單項(xiàng)預(yù)測(cè)模型是不相同的,它采用的方法是選取適當(dāng)?shù)募訖?quán)平均系數(shù),使得這些模型按照設(shè)定好的配置方式進(jìn)行組合,很顯然,有時(shí)候這樣的做法是不符合現(xiàn)實(shí)要求的。在整個(gè)預(yù)測(cè)模型中,如何求解加權(quán)平均系數(shù)是重中之重,論文旨在選取正確的方法進(jìn)行求解,使得整個(gè)預(yù)測(cè)模型的預(yù)測(cè)精度得到顯著提高。本文首先對(duì)課題背景及意義進(jìn)行調(diào)查研究,了解了問(wèn)題的產(chǎn)生并找到解決方案。其次,詳細(xì)了解了相關(guān)預(yù)測(cè)技術(shù)理論,系統(tǒng)學(xué)習(xí)并掌握了確定組合預(yù)測(cè)模型權(quán)重的方式方法。隨后,闡述了基于誤差指標(biāo)的線(xiàn)性和非線(xiàn)性組合預(yù)測(cè)模型,選取多個(gè)合適的實(shí)際案例分別進(jìn)行仿真實(shí)驗(yàn),進(jìn)行了對(duì)比分析。并引入人工蜂群算法來(lái)確定最優(yōu)組合預(yù)測(cè)模型的權(quán)重,其目的是解決確定權(quán)重時(shí)工作量大和無(wú)法保證所求權(quán)重恒大于零的問(wèn)題。最后,通過(guò)引入IOWHA算子,很好地解決了傳統(tǒng)最優(yōu)不變權(quán)組合預(yù)測(cè)模型所存在的問(wèn)題。以此為基礎(chǔ),闡述了二階預(yù)測(cè)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(duì)其進(jìn)行研究與說(shuō)明,最后,結(jié)合實(shí)際案例,進(jìn)行詳細(xì)分析。在預(yù)測(cè)的實(shí)際運(yùn)用中,我們所面臨的預(yù)測(cè)對(duì)象很有可能是一個(gè)極為復(fù)雜的系統(tǒng),所建立的模型具有很強(qiáng)的不確定性,會(huì)大大增加預(yù)測(cè)的風(fēng)險(xiǎn)。通過(guò)多個(gè)實(shí)例分析表明,本文所建立的組合預(yù)測(cè)模型是一個(gè)性能優(yōu)良的模型,它不僅克服了傳統(tǒng)組合預(yù)測(cè)模型的缺陷,而且提高了預(yù)測(cè)精度,能夠很好的應(yīng)用于實(shí)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(xué)位授予單位】：山東大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：TP18

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 王豐效;;加權(quán)幾何平均變權(quán)組合預(yù)測(cè)方法及其應(yīng)用[J];數(shù)學(xué)的實(shí)踐與認(rèn)識(shí);2016年23期

2 吳少華;程朋根;胡智仁;;基于二階預(yù)測(cè)有效度IOWGA算子的變形組合預(yù)測(cè)研究[J];大地測(cè)量與地球動(dòng)力學(xué);2016年09期

3 李思琴;林磊;孫承杰;;基于卷積神經(jīng)網(wǎng)絡(luò)的搜索廣告點(diǎn)擊率預(yù)測(cè)[J];智能計(jì)算機(jī)與應(yīng)用;2015年05期

4 蘇紅柳;唐國(guó)強(qiáng);孫國(guó)華;;二階有效度組合模型預(yù)測(cè)沉降變形研究[J];測(cè)繪科學(xué);2014年11期

5 范成方;史建民;;基于Holt-Winters及趨勢(shì)—ARMA組合模型的糧食種植成本預(yù)測(cè)分析——以山東省玉米、小麥為例[J];中國(guó)農(nóng)業(yè)資源與區(qū)劃;2014年03期

6 鄭莉;段冬梅;陸鳳彬;許偉;楊翠紅;汪壽陽(yáng);;我國(guó)豬肉消費(fèi)需求量集成預(yù)測(cè)——基于ARIMA、VAR和VEC模型的實(shí)證[J];系統(tǒng)工程理論與實(shí)踐;2013年04期

7 陳華;;淺析幾種常用的定性預(yù)測(cè)方法[J];華章;2012年35期

8 溫愛(ài)華;李松;;基于廣義回歸神經(jīng)網(wǎng)絡(luò)的鐵路貨運(yùn)量預(yù)測(cè)[J];鐵道運(yùn)輸與經(jīng)濟(jì);2011年02期

9 張杰;劉小明;賀玉龍;陳永勝;;ARIMA模型在交通事故預(yù)測(cè)中的應(yīng)用[J];北京工業(yè)大學(xué)學(xué)報(bào);2007年12期

10 韓遠(yuǎn)迎;隋忠華;;轉(zhuǎn)變經(jīng)濟(jì)增長(zhǎng)方式——基于VAR和VEC模型的實(shí)證研究[J];科技和產(chǎn)業(yè);2007年04期

相關(guān)碩士學(xué)位論文 前9條

1 于秀偉;組合預(yù)測(cè)中單項(xiàng)預(yù)測(cè)模型的選擇研究[D];長(zhǎng)安大學(xué);2015年

2 肖科;基于BP神經(jīng)網(wǎng)絡(luò)的Web服務(wù)選擇算法[D];北京郵電大學(xué);2014年

3 賈瑞民;人工蜂群算法的改進(jìn)及應(yīng)用研究[D];廣西民族大學(xué);2013年

4 王瑩瑩;基于灰色神經(jīng)網(wǎng)絡(luò)模型的煤炭物流需求預(yù)測(cè)研究[D];北京交通大學(xué);2012年

5 范騰騰;城市道路交通流量短時(shí)預(yù)測(cè)研究[D];北京交通大學(xué);2012年

6 蔣吉麗;基于BP神經(jīng)網(wǎng)絡(luò)的強(qiáng)對(duì)流天氣預(yù)報(bào)模型研究[D];電子科技大學(xué);2010年

7 戴華娟;組合預(yù)測(cè)模型及其應(yīng)用研究[D];中南大學(xué);2007年

8 毛開(kāi)翼;關(guān)于組合預(yù)測(cè)中的權(quán)重確定及應(yīng)用[D];成都理工大學(xué);2007年

9 李彬;徑向基函數(shù)神經(jīng)網(wǎng)絡(luò)的學(xué)習(xí)算法研究[D];山東大學(xué);2005年

，

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