{"title":"通过混合频率机器学习进行旅游预测","authors":"Mingming Hu , Mei Li , Yuxiu Chen , Han Liu","doi":"10.1016/j.tourman.2024.105004","DOIUrl":null,"url":null,"abstract":"<div><p>This study aims to establish the nonlinear relationship between high frequency Baidu search volumes, low frequency tourist arrivals and dummy variables and proposes a mixed-frequency machine learning model: the Bidirectional Long Short-Term Memory Mixed Frequency Data Sampling (BiLSTM-MIDAS) model. The empirical results of forecasting weekly tourist arrivals to Kulangsu and Jiuzhaigou Valley in China demonstrate that (1) BiLSTM-MIDAS can outperform benchmark models, which is also confirmed during the COVID-19 pandemic period; (2) Compared with the MIDAS model, establishing the nonlinear relationship between high frequency Baidu search volumes, low frequency tourist arrivals and dummy variables using BiLSTM-MIDAS can improve the roles of high-frequency search engines in forecasting tourism demand. This study represents the first attempt to apply machine learning methods for tourism demand forecasting with mixed-frequency data.</p></div>","PeriodicalId":48469,"journal":{"name":"Tourism Management","volume":"106 ","pages":"Article 105004"},"PeriodicalIF":10.9000,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0261517724001237/pdfft?md5=e0d7e2fbd59c2629c1349a72d427da87&pid=1-s2.0-S0261517724001237-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Tourism forecasting by mixed-frequency machine learning\",\"authors\":\"Mingming Hu , Mei Li , Yuxiu Chen , Han Liu\",\"doi\":\"10.1016/j.tourman.2024.105004\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>This study aims to establish the nonlinear relationship between high frequency Baidu search volumes, low frequency tourist arrivals and dummy variables and proposes a mixed-frequency machine learning model: the Bidirectional Long Short-Term Memory Mixed Frequency Data Sampling (BiLSTM-MIDAS) model. The empirical results of forecasting weekly tourist arrivals to Kulangsu and Jiuzhaigou Valley in China demonstrate that (1) BiLSTM-MIDAS can outperform benchmark models, which is also confirmed during the COVID-19 pandemic period; (2) Compared with the MIDAS model, establishing the nonlinear relationship between high frequency Baidu search volumes, low frequency tourist arrivals and dummy variables using BiLSTM-MIDAS can improve the roles of high-frequency search engines in forecasting tourism demand. This study represents the first attempt to apply machine learning methods for tourism demand forecasting with mixed-frequency data.</p></div>\",\"PeriodicalId\":48469,\"journal\":{\"name\":\"Tourism Management\",\"volume\":\"106 \",\"pages\":\"Article 105004\"},\"PeriodicalIF\":10.9000,\"publicationDate\":\"2024-07-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S0261517724001237/pdfft?md5=e0d7e2fbd59c2629c1349a72d427da87&pid=1-s2.0-S0261517724001237-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Tourism Management\",\"FirstCategoryId\":\"91\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0261517724001237\",\"RegionNum\":1,\"RegionCategory\":\"管理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENVIRONMENTAL STUDIES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Tourism Management","FirstCategoryId":"91","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0261517724001237","RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENVIRONMENTAL STUDIES","Score":null,"Total":0}
Tourism forecasting by mixed-frequency machine learning
This study aims to establish the nonlinear relationship between high frequency Baidu search volumes, low frequency tourist arrivals and dummy variables and proposes a mixed-frequency machine learning model: the Bidirectional Long Short-Term Memory Mixed Frequency Data Sampling (BiLSTM-MIDAS) model. The empirical results of forecasting weekly tourist arrivals to Kulangsu and Jiuzhaigou Valley in China demonstrate that (1) BiLSTM-MIDAS can outperform benchmark models, which is also confirmed during the COVID-19 pandemic period; (2) Compared with the MIDAS model, establishing the nonlinear relationship between high frequency Baidu search volumes, low frequency tourist arrivals and dummy variables using BiLSTM-MIDAS can improve the roles of high-frequency search engines in forecasting tourism demand. This study represents the first attempt to apply machine learning methods for tourism demand forecasting with mixed-frequency data.
期刊介绍:
Tourism Management, the preeminent scholarly journal, concentrates on the comprehensive management aspects, encompassing planning and policy, within the realm of travel and tourism. Adopting an interdisciplinary perspective, the journal delves into international, national, and regional tourism, addressing various management challenges. Its content mirrors this integrative approach, featuring primary research articles, progress in tourism research, case studies, research notes, discussions on current issues, and book reviews. Emphasizing scholarly rigor, all published papers are expected to contribute to theoretical and/or methodological advancements while offering specific insights relevant to tourism management and policy.