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[학술저널]

  • 학술저널

Seongwook Youn(Korea National University of Transportation) Hyun-chong Cho(Kangwon National University)

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초록

Google Trends provides weekly information on keyword search frequency on the Google search engine. Search volume patterns for the search keyword can also be analyzed based on category and by the location of those making the search. Also, Google provides “Hot searches” and “Top charts” including top and rising searches that include the search keyword. All this information is kept up to date, and allows trend comparisons by providing past weekly figures. In this study, we present a predictive model for TV markets using the searched data in Google search engine (Google Trend data). Using a predictive model for the market and analysis of the Google Trend data, we obtained an efficient and meaningful result for the TV market, and also determined highly ranked countries and cities. This method can provide very useful information for TV manufacturers and others.

목차

Abstract
1. Introduction
2. Google Trends
3. Predictive Model of TV Time Series Data using ARIMA Model
4. Experiment and Results
5. Conclusion
References

참고문헌(13)

  • 1.

    C. E. a. H. C. Hal Varian, “Predicting the Present with Google Trends,” in Google Research Blog, ed, 2009.

  • 2.

    C. Hand and G. Judge, “Searching for the picture: forecasting UK cinema admissions using Google Trends data,” Applied Economics Letters, vol. 19, pp. 1051-1055, 2012/07/01 2011.

  • 3.

    Google, How does Google Trends Work, Official Site, 2010. Available: http://www.google.com/intl/en/trends/ about.html

  • 4.

    H. Choi and H. A. L. Varian, “Predicting the Present with Google Trends,” Economic Record, vol. 88, pp. 2-9, 2012.

  • 5.

    J. Contreras, R. Espinola, F. J. Nogales, and A. J. Conejo, “ARIMA models to predict next-day electricity prices,” Power Systems, IEEE Transactions on, vol. 18, pp. 1014-1020, 2003.

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