基于最小二乘法赋权的ARIMA-LSTM模型预测入境旅游人数——以上海市为例

doi:10.3785/j.issn.1008-9497.2023.04.014

浙江大学学报（理学版）

2023, Vol. 50

Issue (4): 508-520 DOI: 10.3785/j.issn.1008-9497.2023.04.014

旅游学

基于最小二乘法赋权的ARIMA-LSTM模型预测入境旅游人数——以上海市为例

康俊锋¹(

),符悦¹,方雷²(

),李咪咪³,谢玉静²,周朝阳⁴

^1.江西理工大学土木与测绘工程学院，江西赣州 341000
^2.复旦大学环境科学与工程系，上海 200433
^3.香港理工大学酒店及旅游业管理学院，香港 999077
^4.江西省国防科技信息和卫星应用中心，江西南昌 330036

ARIMA-LSTM model based on least square weighting to predict number of inbound tourists: A case study of Shanghai

Junfeng KANG¹(

),Yue FU¹,Lei FANG²(

),Mimi LI³,Yujing XIE²,Chaoyang ZHOU⁴

^1.School of Civil and Surveying & Mapping Engineering，Jiangxi University of Science and Technology，Ganzhou 341000，Jiangxi Province，China
^2.Department of Environmental Science and Engineering，Fudan University，Shanghai 200433，China
^3.School of Hotel and Tourism Management，The Hong Kong Polytechnic University，Hong Kong 999077，China
^4.Jiangxi Provincial Defense Science and Technology Information and Satellite Application Center，Nanchang 330036，China

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摘要：

为降低新冠病毒感染疫情大流行对旅游业的二次冲击，对疫情防控期间入境旅游市场的需求进行准确预测可为后期旅游业复苏提供科学依据。以上海市为研究区域，选取入境旅游人数、主要客源国、谷歌搜索指数、新增确诊病例数等数据，定量分析疫情前后入境旅游人数的空间变化特征及时间变化趋势，并用基于最小二乘法赋权的ARIMA-LSTM模型预测疫情后的入境旅游人数。结果表明：（1）疫情发生前后，亚洲客源市场一直占据入境旅游市场的核心地位，且传统入境游客与非传统入境游客的比例约为9∶1；（2）入境旅游人数与谷歌搜索指数存在长期正相关及格兰杰因果关系，与确诊病例数无明显相关性；（3）通过对比模型评价指标发现，当ARIMA-LSTM模型的 $R 2$ 大于0.8时，拟合较好，预测误差较单一模型小，预测精度较单一模型高，适用于疫情前、中、后期的旅游人数恢复预测；（4）对2021—2024年入境旅游人数进行恢复预测，发现该期间入境旅游人数呈明显的U形曲线。自2022年12月疫情全面放开后，旅游业逐步恢复，预计入境旅游人数在2024年12月恢复至疫情前水平，即需1.5 a的恢复期。

关键词： 新冠病毒感染; 上海旅游预测; ARIMA-LSTM模型; 最小二乘法; 谷歌搜索指数

Abstract:

In order to reduce the secondary impact of the COVID-19 on the tourism industry, accurate prediction of the demand of inbound tourism market during the epidemic period can provide a scientific basis for later recovery and development of tourism. Taking Shanghai as the study area, the number of inbound tourists, major source countries, Google search index, confirmed cases of the epidemic and other data were selected to quantitatively analyze the spatial characteristics and the temporal trend of inbound tourism before and after the epidemic, the ARIMA-LSTM combination model weighted by the least square method was used to predict the number of inbound tourists after the epidemic. The results show that: (1) before and after the outbreak of the epidemic, the Asian tourists occupied the core position of the inbound tourism market, and the proportion of traditional inbound tourists and non-traditional inbound tourists is about 9∶1; (2) the number of inbound tourists demonstrates a long-term positive correlation and Granger causality with the Google search index, but there is no significant correlation with the confirmed cases of the epidemic; (3) by comparing the model evaluation indicators, it is found that when the $R 2$ value of ARIMA-LSTM model is higher than 0.8, the model fits well, and the prediction error is smaller than that of a single model, and the prediction accuracy is higher, which means that the model can be uniformly applied to the recovery prediction of tourist numbers before, during and after the epidemic; (4) the number of inbound tourists from 2021-2024 is predicted, and it shows that the tourism trend during this period presents an obvious U-shaped change. After the comprehensive release of the epidemic in December 2022, the tourism industry began to gradually recover, and it is expected that the number of inbound tourists will return to the preepidemic tourism level by the end of 2024, that is, the recovery period is about one and a half years.

Key words: COVID-19 Shanghai tourism forecast ARIMA-LSTM model least squares method Google index

收稿日期: 2022-08-22 出版日期: 2023-07-17

CLC:

F 590

基金资助: 国家自然科学基金资助项目(42261071);上海市自然科学基金资助项目(21ZR1407600)

通讯作者: 方雷 E-mail: junfeng.kang@jxust.edu.cn;fanglei@fudan.edu.cn

作者简介: 康俊锋（1978—）， ORCID：https：//orcid.org/0000-0002-9887-4632，男，博士，副教授，主要从事高性能GIS算法及应用研究，E-mail：junfeng.kang@jxust.edu.cn.

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引用本文:

康俊锋,符悦,方雷,李咪咪,谢玉静,周朝阳. 基于最小二乘法赋权的ARIMA-LSTM模型预测入境旅游人数——以上海市为例[J]. 浙江大学学报（理学版）, 2023, 50(4): 508-520.

Junfeng KANG,Yue FU,Lei FANG,Mimi LI,Yujing XIE,Chaoyang ZHOU. ARIMA-LSTM model based on least square weighting to predict number of inbound tourists: A case study of Shanghai. Journal of Zhejiang University (Science Edition), 2023, 50(4): 508-520.

链接本文:

https://www.zjujournals.com/sci/CN/10.3785/j.issn.1008-9497.2023.04.014 或 https://www.zjujournals.com/sci/CN/Y2023/V50/I4/508

表1 上海市入境旅游人数及各谷歌搜索指数统计信息

表2 疫情相关数据统计信息

图1 LSTM结构

图2 ARIMA-LSTM模型框架

图3 上海市不同客源国入境游客变化

图4 2017—2020年上海市入境游客主要客源国比例

图5 四大洲入沪旅游人数组成

图6 谷歌搜索关键词筛选

图7 2017—2021年上海市入境旅游人数与谷歌搜索指数趋势对比（归一化）

图8 疫情确诊病例与境外输入病例变化趋势对比

图9 上海市入境旅游人数与关键词谷歌搜索指数、新增确诊病例数的相关系数

表3 格兰杰因果关系检验结果

图10 疫情前后不同模型预测入境旅游人数效果

表4 不同模型疫情前后预测精度评价结果

图11 上海市2022—2024年入境旅游人数预测结果

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