中国外国人入境旅游人数预测分析
An Analysis of Chinese Inbound Tourism Forecasts
DOI: 10.12677/AAM.2023.1211461, PDF,   
作者: 杜青霖:云南财经大学统计与数学学院,云南 昆明
关键词: 外国人入境旅游人数预测分析ARIMA模型Number of Inbound Tourists Prediction Analysis ARIMA Model
摘要: 自改革开放以来,随着中国国际影响力的逐渐扩大,中国的入境旅游人数逐年递增,每年吸引了大量的外国游客来中国参观游玩。而且入境旅游在中国旅游业中有着重要的地位,中国旅游业的产值在中国的国民经济总产值中所占的比重也在逐年提高。然而,自2019年新冠肺炎疫情爆发以来,中国的外国人入境旅游人数呈现直线式下降,疫情的爆发严重影响了入境旅游人数。因此,对于外国人入境旅游人数的预测有助于中国各地旅游业提前做好旅游接待战略。故本文以1990~2020为样本时期,利用R软件建立ARIMA模型对中国2021年~2025年的外国人入境旅游人数进行预测。研究发现,该模型对2021年和2022年中国入境旅游人数的预测值与实际值相差较小。因此,该模型能够较好的预测中国2023~2025年的入境旅游人数,并对中国旅游业的发展提出建议与意见。
Abstract: Since the reform and opening up, with the gradual expansion of China’s international influence, the number of Chinese inbound tourists has been increasing year by year, attracting a large number of foreign tourists to visit and play in China every year. Moreover, inbound tourism plays an important role in Chinese tourism industry, and the proportion of Chinese tourism industry’s output value in the total national economic output value is also increasing year by year. However, since the out-break of the COVID-19 in 2019, the number of Chinese inbound tourists has declined in a straight line, and the outbreak of the epidemic has seriously affected the number of inbound tourists. Therefore, predicting the number of inbound tourists for foreigners can help the tourism industry in various regions of Chinese prepare tourism reception strategies in advance. Therefore, this arti-cle takes 1990 to 2020 as the sample period and uses R software to establish an ARIMA model to predict the number of Chinese inbound foreign tourists from 2021 to 2025. Research has found that the predicted values of the model for the number of inbound tourists in 2021 and 2022 differ slightly from the actual values. Therefore, this model can effectively predict the number of Chinese inbound tourists from 2023 to 2025, and provide suggestions and opinions on the development of Chinese tourism industry.
文章引用:杜青霖. 中国外国人入境旅游人数预测分析[J]. 应用数学进展, 2023, 12(11): 4686-4696. https://doi.org/10.12677/AAM.2023.1211461

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