Short-term forecasting of Japanese tourist inflow to South Korea using Google trends data. Issue 3 (24th March 2017)

• Record Type: Journal Article
• Authors: Park, Sangkon; Lee, Jungmin; Song, Wonho
• Is Part Of: Journal of travel & tourism marketing. Volume 34:Issue 3(2017)
• Page Start: 357
• Page End: 368
• Publication Date: 2017-03-24
• Subjects: Tourism -- internet search data -- Google trends -- multiplicative seasonal autoregressive integrated moving average -- ARIMA -- forecasting; Tourism -- Periodicals; 338.47910688
• DOI: 10.1080/10548408.2016.1170651
• Languages: English
• ISSNs: 1054-8408

Abstract

ABSTRACT: We utilize the Internet search data from Google Trends to provide short-term forecasts for the inflow of Japanese tourists to South Korea. We construct the Google variable in a systematic way by combining keywords to minimize mean squared or mean absolute forecasting errors. We augment the Google variable to the standard time-series forecasting models and compare their forecasting accuracies. We find that Google-augmented models perform much better than the standard time-series models in terms of short-term forecasting accuracy. In particular, Google models show better out-of-sample forecasting performance than in-sample forecasting.