[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119478-en":3,"doc-seo-119478-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119478,962075114765,"Quinn","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Forecasting Trends in Foreign Tourism by Machine Learning","Online search engines strongly influence travel planning, turning keyword search activity into a forward-looking indicator of tourism demand. This study applies machine learning to evaluate how keyword search data can predict tourist arrivals, using a time lag between searches and arrivals. Data are compiled from a search engine and Thai government sources for 2014–2019. SARIMA models forecast search trends and tourist numbers, while SVM and Random Forest deliver superior arrival prediction performance.","Jirapond Muangprathub / Patthamaphon Kaewmanee / Jarunee Saelee / Pattaraporn Warintarawej / Wichuta Sae-jie Forecasting Trends in Foreign Tourism by Machine Learning  \nAbstract  \nTourists frequently use online search engines for travel planning, making search data a valuable predictor of future tourism volume. This study employs machine learning to analyse the predictive power of keyword search data for forecasting tourist arrivals, incorporating a lag time between searches and arrivals. The dataset is collected and prepared from two sources: a search engine and government agencies, covering the years 2014-2019, to be analysed by machine learning. The SARIMA model effectively forecasts trends in keyword searches and tourist numbers, while SVM (Support Vector Machine) and Random Forest outperform other methods in predicting arrivals. This research supports tourism operators and stakeholders in planning for future tourists, utilising the obtained keywords to enhance visibility in tourist searches through SEO. Keywords: tourism volume forecasting, tourist arrivals prediction, machine learning, search engine data, tourist data analysis  \n1. Introduction  \nIn the digital era, online search engines have become essential tools for travel planning, with tourists frequently relying on them to explore destinations, accommodations, and attractions. The widespread use of search engines has made search data a valuable source for predicting future tourism demand (Zervas et al., 2017; Cheng et al., 2018). The present study focuses on using keyword search data from Google Trends to forecast tourist arrivals. By analysing the relationship between search volumes and actual tourist numbers, this research seeks to improve forecasting accuracy and provide valuable insights for stakeholders in the tourism industry. In Thailand, the number of visitors has shown remarkable growth, increasing from 14.1 million in 2009 to 38.3 million in 2018 (Adulwattana et al., 2019) . This surge highlights the growing significance of tourism as a key driver of Thailand’s GDP growth. In 2019, the tourism sector played a pivotal role in the Thai economy, generating 86,908 million dollars (3.01 trillion baht) in revenue from 39.7 million foreign tourists. The country’s exceptional tourism potential has contributed to its attractiveness as a destination for international visitors (Wongsathan et al., 2018). The tourism sector is financially strong and has achieved good results, with effects that extend across various sectors, including restaurants, large and small shops, farmers, and transportation businesses. Planning marketing strategies for entrepreneurs in the tourism industry is essential to attract tourists (Kaewmanee et al., 2021). The search engine tool is primarily used to support travel  \nJirapond Muangprathub, PhD, Associate Professor, Faculty of Science and Industrial Technology, Prince of Songkla University, Surat Thani Campus, Surat Thani, Thailand; ORCID ID: [https://orcid.org/0000-0002-3062-4696](https://orcid.org/0000-0002-3062-4696); [e-mail: jirapond.m@psu.ac.th](e-mail: jirapond.m@psu.ac.th)  \nPatthamaphon Kaewmanee, MSc, Faculty of Science and Industrial Technology, Prince of Songkla University, Surat Thani Campus, Surat Thani, Thailand; [e-mail: 6240320502@psu.ac.th](e-mail: 6240320502@psu.ac.th)  \nJarunee Saelee, PhD, Faculty of Science and Technology, Prince of Songkla University, Pattani Campus, Pattani, Thailand;  \nORCID ID: [https://orcid.org/0000-0002-4925-4170](https://orcid.org/0000-0002-4925-4170); [e-mail: jarunee.sa@psu.ac.th](e-mail: jarunee.sa@psu.ac.th)  \nPattaraporn Warintarawej, PhD, Faculty of Science and Industrial Technology, Prince of Songkla University, Surat Thani Campus, Surat Thani, Thailand; ORCID ID: [https://orcid.org/0000-0002-2034-1932](https://orcid.org/0000-0002-2034-1932); [e-mail: pattaraporn.w@psu.ac.th](e-mail: pattaraporn.w@psu.ac.th)  \nWichuta Sae-jie, PhD, Corresponding Author, Assistant Professor, Faculty of S","cbCaicU0ApcDPuP6","https://ap.wps.com/l/cbCaicU0ApcDPuP6","pdf",2162859,1,18,"English","en",105,"# Introduction\n## Tourism demand forecasting using search data\n## Research approach and data sources\n## Comparative machine learning methods","[{\"question\":\"Why are online search engines useful for forecasting foreign tourism demand?\",\"answer\":\"Tourists frequently use search engines to plan trips, so search activity reflects emerging travel interests. Search data therefore provides signals for anticipating future tourist volume and demand shifts.\"},{\"question\":\"What data sources and time range are used in the study?\",\"answer\":\"The research uses keyword search index data from Google Trends and foreign tourist volume data from Thai government sources. The covered period is 2014–2019.\"},{\"question\":\"Which machine learning models perform best for predicting tourist arrivals?\",\"answer\":\"SARIMA effectively forecasts keyword search trends and tourist numbers, while SVM and Random Forest outperform other methods in predicting arrivals based on the prepared dataset and lag structure.\"}]","Forecasting Trends in Foreign Tourism by Machine Learning | PDF",1785724517,45,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"forecasting-trends-in-foreign-tourism-by-machine-learning","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/forecasting-trends-in-foreign-tourism-by-machine-learning/119478/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05","2026-08-03",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why are online search engines useful for forecasting foreign tourism demand?","Question",{"text":76,"@type":77},"Tourists frequently use search engines to plan trips, so search activity reflects emerging travel interests. Search data therefore provides signals for anticipating future tourist volume and demand shifts.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What data sources and time range are used in the study?",{"text":81,"@type":77},"The research uses keyword search index data from Google Trends and foreign tourist volume data from Thai government sources. The covered period is 2014–2019.",{"name":83,"@type":74,"acceptedAnswer":84},"Which machine learning models perform best for predicting tourist arrivals?",{"text":85,"@type":77},"SARIMA effectively forecasts keyword search trends and tourist numbers, while SVM and Random Forest outperform other methods in predicting arrivals based on the prepared dataset and lag structure.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]