مقاله انگلیسی رایگان در مورد پیش بینی ورود گردشگران با یادگیری ماشین – الزویر ۲۰۱۹

elsevier

 

مشخصات مقاله
ترجمه عنوان مقاله پیش بینی ورود گردشگران با یادگیری ماشین و جست و جوی فهرست اینترنت
عنوان انگلیسی مقاله Forecasting tourist arrivals with machine learning and internet search index
انتشار مقاله سال ۲۰۱۹
تعداد صفحات مقاله انگلیسی ۱۰ صفحه
هزینه دانلود مقاله انگلیسی رایگان میباشد.
پایگاه داده نشریه الزویر
نوع نگارش مقاله مقاله پژوهشی (Research article)
مقاله بیس این مقاله بیس نمیباشد
نمایه (index) scopus – master journals – JCR
نوع مقاله ISI
فرمت مقاله انگلیسی  PDF
ایمپکت فاکتور(IF) ۵٫۹۲۱ در سال ۲۰۱۷
شاخص H_index ۱۴۳ در سال ۲۰۱۹
شاخص SJR ۳٫۰۲۷ در سال ۲۰۱۹
رشته های مرتبط گردشگری و توریسم، کامپیوتر، فناوری اطلاعات
گرایش های مرتبط مدیریت گردشگری، هوش مصنوعی، اینترنت و شبکه های گسترده
نوع ارائه مقاله ژورنال
مجله / کنفرانس مدیریت گردشگری – Tourism Management
دانشگاه Academy of Mathematics and Systems Science – Beijing – China
کلمات کلیدی پیش بینی تقاضای گردشگری، یادگیری ماشین حداکثری کرنل، اطلاعات پرس و جو و تحقیقی، تحلیل کلان داده، شاخص جستجوی کامپوزیت
کلمات کلیدی انگلیسی Tourism demand forecasting, Kernel extreme learning machine, Search query data, Big data analytics, Composite search index
شناسه دیجیتال – doi
https://doi.org/10.1016/j.tourman.2018.07.010
کد محصول E9411
وضعیت ترجمه مقاله  ترجمه آماده این مقاله موجود نمیباشد. میتوانید از طریق دکمه پایین سفارش دهید.
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فهرست مطالب مقاله:
Abstract
۱ Introduction
۲ Literature review
۳ Kernel extreme learning machine
۴ Forecasting framework
۵ Experimental study
۶ Conclusions
References

بخشی از متن مقاله:
ABSTRACT

Previous studies have shown that online data, such as search engine queries, is a new source of data that can be used to forecast tourism demand. In this study, we propose a forecasting framework that uses machine learning and internet search indexes to forecast tourist arrivals for popular destinations in China and compared its forecasting performance to the search results generated by Google and Baidu, respectively. This study verifies the Granger causality and co-integration relationship between internet search index and tourist arrivals of Beijing. Our experimental results suggest that compared with benchmark models, the proposed kernel extreme learning machine (KELM) models, which integrate tourist volume series with Baidu Index and Google Index, can improve the forecasting performance significantly in terms of both forecasting accuracy and robustness analysis.

Introduction

All over the world, the tourism industry contributes significantly to economic growth (Gunter & Onder, 2015; Song, Li, Witt, & Athanasopoulos, 2011). According to the China National Tourism Administration, in 2016 the tourism income of China reached 4.69 trillion RMB, increasing by 13.6% compared to the previous year, and accounted for 6.3% of China’s GDP. Thus, forecasting tourist volume is becoming increasingly important for predicting future economic development. Tourism demand forecasting may provide basic information for subsequent planning and policy making (Chu, 2008; Witt & Song, 2002). Methods used in tourism modeling and forecasting fall into four groups: time series models, econometrics models, artificial intelligence techniques and qualitative methods (Goh & Law, 2011; Song & Li, 2008). In addition to simple tourist data announced by the State Statistics Bureau, Internet search queries, which reflect the behavior and intentions of tourists, have increasingly been used in tourism forecasting models (Croce, 2017; Goodwin, 2008). However, the search index has created big opportunities in the modeling process of tourism forecasting (Li, Pan, Raw & Huang, 2017). Internet search data has been applied to many aspects, such as hotel registrations (Pan & Yang, 2017; Rivera, 2016), tourist numbers (Bangwayo-Skeete & Skeete, 2015; Yang, Pan, Evans, & Lv, 2015), economic indicators (Choi & Varian, 2012), unemployment rates (Askitas & Zimmermann, 2009), private consumption (Vosen & Schmidt, 2011), and stock returns (Zhu & Bao, 2014). When introducing the Baidu Index or Google Index into forecasting models, keywords and the composition of indexes must be selected carefully. Keywords can be selected according to the correlation coefficient, the tendency chart or the crowd-squared method (Brynjolfsson, Geva, & Reichman, 2016). Additionally, the composition of indexes can be achieved by the HE-TDC method (Peng, Liu, Wang, & Gu, 2017) or the principal component analysis (PCA). Obviously, efforts should be made to avoid problems related to multi-collinearity and over-fitting to the greatest extent possible. In this study, we proposed a new framework integrating machine learning and Internet search index to forecast tourist volume. The forecasting power of the framework is attributable to two features: first, relevant Internet search queries greatly contribute to the goodness of fit; second, Kernel-based extreme learning machines have short computing time and good generalization ability. However, as far as we know, few studies have adopted extreme learning machine to forecast tourism demand. The proposed framework is utilized to forecasting Beijing tourist arrivals. Relevant Internet search keywords cover the various aspects of tourism including dining, lodging, recreation, shopping, tour and traffic. Different from previous studies, this paper considers both Baidu Index and Google Index, which reflect the current situation of domestic tourists and foreign travelers.

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