Forecasting Tourist Arrivals with Machine Learning and Internet Search Index

Shaolong Sun; Shouyang Wang; Yunjie Wei; Xianduan Yang; Kwok-Leung Tsui

doi:10.1109/bigdata.2017.8258439

Forecasting Tourist Arrivals with Machine Learning and Internet Search Index

Shaolong Sun, Shouyang Wang, Yunjie Wei^*, Xianduan Yang, Kwok-Leung Tsui

^*Corresponding author for this work

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

6 Citations (Scopus)

Abstract

The queries entered into search engines register hundreds of millions of different searches by tourists, not only reflecting the trends of the searchers' preferences for travel products, but also offering a forecasting of their future travel behavior. This paper proposed a forecasting framework based on internet search index and machine learning to forecast tourist arrivals, and compared the forecasting performance of two different search engines data, Baidu and Google. The empirical results suggest that the proposed KELM models by fusing Baidu index and Google index can significantly improve the forecasting performance and outperform other benchmark models in terms of forecasting accuracy.

Original language	English
Title of host publication	2017 IEEE International Conference on Big Data
Subtitle of host publication	Proceedings
Publisher	IEEE
Pages	4165-4169
ISBN (Electronic)	9781538627150, 9781538627143
ISBN (Print)	9781538627167
DOIs	https://doi.org/10.1109/bigdata.2017.8258439
Publication status	Published - Dec 2017
Event	5th IEEE International Conference on Big Data (IEEE Big Data 2017) - Boston, United States Duration: 11 Dec 2017 → 14 Dec 2017

Conference

Conference	5th IEEE International Conference on Big Data (IEEE Big Data 2017)
Place	United States
City	Boston
Period	11/12/17 → 14/12/17

Research Keywords

big data analytics
composite search index
kernel extreme learning machine
search query data
Tourism forecasting

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10.1109/bigdata.2017.8258439

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@inproceedings{dfa1b9b4eb154aa58993a5b006696238,

title = "Forecasting Tourist Arrivals with Machine Learning and Internet Search Index",

abstract = "The queries entered into search engines register hundreds of millions of different searches by tourists, not only reflecting the trends of the searchers' preferences for travel products, but also offering a forecasting of their future travel behavior. This paper proposed a forecasting framework based on internet search index and machine learning to forecast tourist arrivals, and compared the forecasting performance of two different search engines data, Baidu and Google. The empirical results suggest that the proposed KELM models by fusing Baidu index and Google index can significantly improve the forecasting performance and outperform other benchmark models in terms of forecasting accuracy.",

keywords = "big data analytics, composite search index, kernel extreme learning machine, search query data, Tourism forecasting",

author = "Shaolong Sun and Shouyang Wang and Yunjie Wei and Xianduan Yang and Kwok-Leung Tsui",

year = "2017",

month = dec,

doi = "10.1109/bigdata.2017.8258439",

language = "English",

isbn = "9781538627167",

pages = "4165--4169",

booktitle = "2017 IEEE International Conference on Big Data",

publisher = "IEEE",

address = "United States",

note = "5th IEEE International Conference on Big Data (IEEE Big Data 2017) ; Conference date: 11-12-2017 Through 14-12-2017",

}

Sun, S, Wang, S, Wei, Y, Yang, X & Tsui, K-L 2017, Forecasting Tourist Arrivals with Machine Learning and Internet Search Index. in 2017 IEEE International Conference on Big Data: Proceedings. IEEE, pp. 4165-4169, 5th IEEE International Conference on Big Data (IEEE Big Data 2017), Boston, California, United States, 11/12/17. https://doi.org/10.1109/bigdata.2017.8258439

TY - GEN

T1 - Forecasting Tourist Arrivals with Machine Learning and Internet Search Index

AU - Sun, Shaolong

AU - Wang, Shouyang

AU - Wei, Yunjie

AU - Yang, Xianduan

AU - Tsui, Kwok-Leung

PY - 2017/12

Y1 - 2017/12

N2 - The queries entered into search engines register hundreds of millions of different searches by tourists, not only reflecting the trends of the searchers' preferences for travel products, but also offering a forecasting of their future travel behavior. This paper proposed a forecasting framework based on internet search index and machine learning to forecast tourist arrivals, and compared the forecasting performance of two different search engines data, Baidu and Google. The empirical results suggest that the proposed KELM models by fusing Baidu index and Google index can significantly improve the forecasting performance and outperform other benchmark models in terms of forecasting accuracy.

AB - The queries entered into search engines register hundreds of millions of different searches by tourists, not only reflecting the trends of the searchers' preferences for travel products, but also offering a forecasting of their future travel behavior. This paper proposed a forecasting framework based on internet search index and machine learning to forecast tourist arrivals, and compared the forecasting performance of two different search engines data, Baidu and Google. The empirical results suggest that the proposed KELM models by fusing Baidu index and Google index can significantly improve the forecasting performance and outperform other benchmark models in terms of forecasting accuracy.

KW - big data analytics

KW - composite search index

KW - kernel extreme learning machine

KW - search query data

KW - Tourism forecasting

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DO - 10.1109/bigdata.2017.8258439

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 9781538627167

SP - 4165

EP - 4169

BT - 2017 IEEE International Conference on Big Data

PB - IEEE

T2 - 5th IEEE International Conference on Big Data (IEEE Big Data 2017)

Y2 - 11 December 2017 through 14 December 2017

ER -

Forecasting Tourist Arrivals with Machine Learning and Internet Search Index

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Research Keywords

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