基于模糊理论的国内旅游需求预测研究

发布时间：2018-10-12 21:50

【摘要】：近年来,旅游业持续以较快的速度发展,已经成为世界发展势头最为强劲的产业之一。旅游业的强劲发展带动了经济的快速发展,越来越多的国家开始大力投资开发本国旅游业,将其作为支柱性产业以期能带动整个社会的发展。旅游需求预测能够为国家旅游管理部门在制定战略规划和政策时提供参考依据,为旅游企业的发展和改革提供参考,引导我国旅游市场资源的优化配置。旅游产品的特殊性决定了旅游需求影响因素众多,因此旅游需求预测的影响因素变得更加复杂,还没有一种较好的需求预测方法处理影响因素的复杂性。本文旨在探索出一种更适用于旅游复杂环境的预测方法,以提高旅游预测结果的准确度,并探索出我国国内旅游的发展规律,以便更好地为国内旅游管理和旅游决策等工作服务。本文首先改进了模糊时间序列模型在旅游需求预测时采用等间隔论域划分方法的问题,提出将模糊聚类算法用于论域的非等分划分。然后,针对传统灰色理论预测模型较易受到研究对象样本数据的变化的干扰这一缺点,结合马尔可夫链法适合于预测随机波动较大的系统对象的优点,融入模糊分类理论,在预测后期使用模糊分类法,提出了模糊灰色马尔可夫链法的国内旅游需求预测模型;最后,针对当前国内旅游需求预测大多采用单一预测法,预测结果的准确性和稳定性偏低的情况,引入一种诱导有序加权平均算子法,在本文第二章和第三章建立的单独预测模型的基础上,建立了基于IOWA算子的综合预测模型,并将其用于国内旅游需求预测。研究结果表明,改进的模糊时间序列模型在保证较高的预测精度的同时,简化了计算,且避免了主观设定聚类数而导致的误差;建立的模糊灰色马尔可夫链法的国内旅游需求预测模型能够充分反映历史数据的发展趋势,当历史数据发生较大波动时也能保证较高的预测精度;建立的组合预测模型能比单一模型预测涵盖更多的信息,且能考虑单一模型在不同时期的预测精度的变化,使得预测结果的精度和稳定性得到了进一步的提高。
[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.
【学位授予单位】：湖南工业大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：F592

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