Short-term forecasting of Japanese tourist inflow to South Korea using Google trends data

Park, Sangkon; Lee, Jungmin; Song, Wonho

doi:10.1080/10548408.2016.1170651

Cited by 106 publications

(87 citation statements)

References 33 publications

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“…In addition, there are gray system theory [11] and the synthetic index approach [12] to predict the number of tourists. Park et al [13] used the index of Google search engine to make a short-term prediction of the number of Japanese visitors to South Korea, and believed that the prediction effect of Google augmented model was better than the ordinary time-series models. Hence, Internet search index data has been used to predict the number of tourists, but the average error rate was high and the prediction accuracy was poor.…”

Section: Literature Review 21 Prediction Methods For Tourist Volumementioning

confidence: 99%

The Role of Internet Search Index for Tourist Volume Prediction Based on GDFM Model

Wang

et al. 2020

Teh. vjesn

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Tourist volume is increasing with the expansion of the scale of tourism, and improving the prediction of tourist volume is helpful for tourism managers to make decisions. Internet search index can be applied to predict the behavior of users, which is widely used in the study of tourist volume prediction and infectious disease prediction. However, the high dimension and correlation of Internet search index tends to reduce the accuracy of the models, which increases the average prediction error of common time-series models. The dynamic factor model (DFM) proposed in our study can be used to solve the problem. This study selects 23 variables and introduces the generalized dynamic factor model (GDFM) to predict tourist volume. The model cannot only reduce the dimensionality of high-dimensional Internet search index data, but also reflects the dynamic correlation between Internet search index data. The results show that the prediction accuracy is improved in our method, and the prediction accuracy of tourist volume is improved by over 10%, with an average error of only 4.3% when compared with the neural network (NN) model. Our study not only provides implications for decision-makers to predict tourist volume timely and accurately, but also helps companies understand tourist' behavior and make the best strategic decisions.

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Section: Literature Review 21 Prediction Methods For Tourist Volumementioning

confidence: 99%

The Role of Internet Search Index for Tourist Volume Prediction Based on GDFM Model

Wang

et al. 2020

Teh. vjesn

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“…Researchers have argued that Google Trends, as a concurrent indicator, could promote more precise forecasting in Switzerland (Siliverstovs and Wochner, 2017) and that a strong correlation exists between hotel visitors and Google search queries in Puerto Rico (Rivera, 2016). Park et al (2017) focus on short-term forecasting of tourist outflows from South Korea to Japan. They claim that Google Trends data not only improve the precision of tourism demand forecasting but also that the out-of-sample forecasting performance outperforms in-sample forecasting with Google Trends.…”

Section: Literature Reviewmentioning

confidence: 99%

Forecasting tourist arrivals: Google Trends meets mixed-frequency data

Havránek

Zeynalov

2019

Tourism Economics

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In this article, we examine the usefulness of Google Trends data in predicting monthly tourist arrivals and overnight stays in Prague during the period between January 2010 and December 2016. We offer two contributions. First, we analyze whether Google Trends provides significant forecasting improvements over models without search data. Second, we assess whether a high-frequency variable (weekly Google Trends) is more useful for accurate forecasting than a low-frequency variable (monthly tourist arrivals) using mixed-data sampling (MIDAS). Our results suggest the potential of Google Trends to offer more accurate predictions in the context of tourism: we find that Google Trends information, both 2 months and 1 week ahead of arrivals, is useful for predicting the actual number of tourist arrivals. The MIDAS forecasting model employing weekly Google Trends data outperforms models using monthly Google Trends data and models without Google Trends data.

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“…To better exhibit and exploit search query data, relevant search exploit services based on search query data are produced, typically as Google Trend (www.google.com/trends/) and Baidu Index (http://index.baidu.com/). A series of researches have been conducted to analyze data from Google Trend and Baidu Index; the robustness and effectiveness of them have been assessed [37][38][39]. In China, compared to Google, which is the largest search engine in the world, Baidu shares more internet search engine market.…”

Section: Selection Of Search Abstract From Baidu Indexmentioning

confidence: 99%

The Relationship Exploration between Public Migration Attention and Population Migration from a Perspective of Search Query

Chun

Duan

2020

IJERPH

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Rapid population migration has been viewed as a critical factor impacting urban network construction and regional sustainable development. The supervision and analysis of population migration are necessary for guiding the optimal allocation of urban resources and for attaining the high efficiency development of region. Currently, the explorations of population migration are often restricted by the limitation of data. In the information era, search engines widely collect public attention, implying potential individual actions, and freely provide open, timelier, and large-scope search query data for helping explore regional phenomena and problems. In this paper, we endeavor to explore the possibility of adopting such data to depict population migration. Based on the search query from Baidu search engine, three migration attention indexes (MAIs) are constructed to capture public migration attention in cyber space. Taking three major urban agglomerations in China as case study, we conduct the correlation analysis among the cyber MAIs and population migration in geographical space. Results have shown that external-MAI and local-MAI can positively reflect the population migration inner regions and across regions from a holistic lens and that intercity-MAI can be a helpful supplement for the delineation of specific population flow. Along with the accumulation of cyber search query data, its potential in exploring population migration can be further reinforced.

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Short-term forecasting of Japanese tourist inflow to South Korea using Google trends data

Cited by 106 publications

References 33 publications

The Role of Internet Search Index for Tourist Volume Prediction Based on GDFM Model

The Role of Internet Search Index for Tourist Volume Prediction Based on GDFM Model

Forecasting tourist arrivals: Google Trends meets mixed-frequency data

The Relationship Exploration between Public Migration Attention and Population Migration from a Perspective of Search Query

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