【摘要】：近年來,旅游業(yè)持續(xù)以較快的速度發(fā)展,已經(jīng)成為世界發(fā)展勢(shì)頭最為強(qiáng)勁的產(chǎn)業(yè)之一。旅游業(yè)的強(qiáng)勁發(fā)展帶動(dòng)了經(jīng)濟(jì)的快速發(fā)展,越來越多的國家開始大力投資開發(fā)本國旅游業(yè),將其作為支柱性產(chǎn)業(yè)以期能帶動(dòng)整個(gè)社會(huì)的發(fā)展。旅游需求預(yù)測(cè)能夠?yàn)閲衣糜喂芾聿块T在制定戰(zhàn)略規(guī)劃和政策時(shí)提供參考依據(jù),為旅游企業(yè)的發(fā)展和改革提供參考,引導(dǎo)我國旅游市場(chǎng)資源的優(yōu)化配置。旅游產(chǎn)品的特殊性決定了旅游需求影響因素眾多,因此旅游需求預(yù)測(cè)的影響因素變得更加復(fù)雜,還沒有一種較好的需求預(yù)測(cè)方法處理影響因素的復(fù)雜性。本文旨在探索出一種更適用于旅游復(fù)雜環(huán)境的預(yù)測(cè)方法,以提高旅游預(yù)測(cè)結(jié)果的準(zhǔn)確度,并探索出我國國內(nèi)旅游的發(fā)展規(guī)律,以便更好地為國內(nèi)旅游管理和旅游決策等工作服務(wù)。本文首先改進(jìn)了模糊時(shí)間序列模型在旅游需求預(yù)測(cè)時(shí)采用等間隔論域劃分方法的問題,提出將模糊聚類算法用于論域的非等分劃分。然后,針對(duì)傳統(tǒng)灰色理論預(yù)測(cè)模型較易受到研究對(duì)象樣本數(shù)據(jù)的變化的干擾這一缺點(diǎn),結(jié)合馬爾可夫鏈法適合于預(yù)測(cè)隨機(jī)波動(dòng)較大的系統(tǒng)對(duì)象的優(yōu)點(diǎn),融入模糊分類理論,在預(yù)測(cè)后期使用模糊分類法,提出了模糊灰色馬爾可夫鏈法的國內(nèi)旅游需求預(yù)測(cè)模型;最后,針對(duì)當(dāng)前國內(nèi)旅游需求預(yù)測(cè)大多采用單一預(yù)測(cè)法,預(yù)測(cè)結(jié)果的準(zhǔn)確性和穩(wěn)定性偏低的情況,引入一種誘導(dǎo)有序加權(quán)平均算子法,在本文第二章和第三章建立的單獨(dú)預(yù)測(cè)模型的基礎(chǔ)上,建立了基于IOWA算子的綜合預(yù)測(cè)模型,并將其用于國內(nèi)旅游需求預(yù)測(cè)。研究結(jié)果表明,改進(jìn)的模糊時(shí)間序列模型在保證較高的預(yù)測(cè)精度的同時(shí),簡化了計(jì)算,且避免了主觀設(shè)定聚類數(shù)而導(dǎo)致的誤差;建立的模糊灰色馬爾可夫鏈法的國內(nèi)旅游需求預(yù)測(cè)模型能夠充分反映歷史數(shù)據(jù)的發(fā)展趨勢(shì),當(dāng)歷史數(shù)據(jù)發(fā)生較大波動(dòng)時(shí)也能保證較高的預(yù)測(cè)精度;建立的組合預(yù)測(cè)模型能比單一模型預(yù)測(cè)涵蓋更多的信息,且能考慮單一模型在不同時(shí)期的預(yù)測(cè)精度的變化,使得預(yù)測(cè)結(jié)果的精度和穩(wěn)定性得到了進(jìn)一步的提高。
[Abstract]:In recent years, tourism continues to develop at a faster rate and has become one of the most powerful industries in the world. The strong development of tourism has driven the rapid development of economy. More and more countries have begun to invest heavily to develop their own tourism industry as a pillar industry in order to promote the development of the whole society. The forecast of tourism demand can provide reference basis for the national tourism management department in formulating strategic planning and policy, provide reference for the development and reform of tourism enterprises, and guide the optimal allocation of tourism market resources in China. The particularity of tourism products determines that there are many factors affecting tourism demand, so the influence factors of tourism demand forecasting become more complex, and there is not a better demand forecasting method to deal with the complexity of influencing factors. The purpose of this paper is to explore a forecasting method that is more suitable for the complex environment of tourism, in order to improve the accuracy of the results of tourism prediction, and to explore the development law of domestic tourism in China. In order to better serve for domestic tourism management and tourism decision-making and other work. In this paper, we first improve the problem that the fuzzy time series model adopts the equal-interval domain partition method when forecasting the tourism demand, and put forward the application of fuzzy clustering algorithm to the non-equipartition division of the domain. Then, aiming at the disadvantage that the traditional grey theory prediction model is easily disturbed by the change of sample data, combining the advantages of Markov chain method, which is suitable for predicting the system objects with large random fluctuation, the fuzzy classification theory is incorporated into the prediction model. In the later stage of forecasting, the fuzzy classification method is used to put forward the forecasting model of domestic tourism demand based on fuzzy grey Markov chain method. When the accuracy and stability of the prediction results are on the low side, an induced ordered weighted average operator method is introduced. Based on the separate prediction model established in the second and third chapters, a comprehensive prediction model based on IOWA operator is established. And it is used to forecast the domestic tourism demand. The results show that the improved fuzzy time series model not only ensures high prediction accuracy, but also simplifies the calculation, and avoids the error caused by the subjective setting of clustering number. The forecast model of domestic tourism demand based on fuzzy grey Markov chain method can fully reflect the development trend of historical data, and it can also ensure higher prediction accuracy when historical data fluctuate greatly. The combined prediction model can cover more information than the single model, and it can take into account the variation of the prediction accuracy in different periods, so the accuracy and stability of the prediction results are further improved.
【學(xué)位授予單位】：湖南工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：F592

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