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新冠疫情背景下福建省旅游业发展预测分析——基于动态组合模型

Prediction and Analysis of Tourism Development in Fujian Province Under the Background of the Novel Coronavirus Pneumonia(COVID-19) Epidemic——Based on the Dynamic Combination Model
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摘要 【目的/意义】预测新冠疫情背景下福建省旅游业发展趋势,为推动福建省旅游业的发展提供建议。【方法/过程】基于2020、2021年新冠疫情下福建省旅游相关数据,依托时间序列预测方法与灰色预测方法相结合构建的动态预测组合模型,对比分析福建省旅游业发展预测数值与实际情况,判断福建省旅游业的发展趋势。【结果/结论】结果显示,2020年后福建省旅游业发展趋势受到影响,但疫情期间福建省旅游业发展趋势向好,建议福建省政府提升旅游服务水平,加强旅游宣传和旅游配套服务,鼓励中小旅游企业自救,创新服务方法。 【Objective/Meaning】 This paper aimed to predict the development trend of tourism in Fujian Province under the background of the novel coronavirus pneumonia(COVID-19) epidemic, in order to provide suggestions for promoting the development of tourism in Fujian Province.【Methods/Procedures】Based on the relevant data of tourism in Fujian Province in2020 and 2021 under the COVID-19 epidemic, the dynamic prediction combination model was constructed by combining the time series prediction method and grey prediction method, and then the prediction value and actual situation of the tourism development in Fujian Province were compared and analyzed, thus to judge the development trend of tourism in Fujian Province.【Results/Conclusions】The results showed that the development trend of tourism in Fujian Province was affected after 2020, but the development trend of tourism in Fujian Province was positive during the epidemic period. It was suggested that the government in Fujian should improve the level of tourism service, strengthen the tourism publicity and tourism supporting services, encourage the small and medium-sized tourism enterprises to help themselves and innovate the service methods.
作者 丛海龙 郑茜 卢素兰 CONG Hai-long;ZHENG Qian;LU Su-lan(Rual Revitalization Institute,Fujian Agriculture and Forestry University,Fuzhou,Fujian 350002,China)
出处 《台湾农业探索》 2022年第5期57-63,共7页 Taiwan Agricultural Research
基金 2021福建省科技厅创新战略项目(2021R0032) 2021年福建农林大学科技创新科目(128-KCX21F07A)。
关键词 新冠疫情 旅游业 时间序列预测 灰色GM(1 1)预测 动态组合预测 COVID-19 epidemic tourism time series prediction grey GM(1 1)prediction dynamic combination forecast
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