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Nowcast of TV Market using Google Trend Data
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 Title & Authors
Nowcast of TV Market using Google Trend Data
Youn, Seongwook; Cho, Hyun-chong;
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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.
ARIMA;Google trend;Nowcast;TV;
 Cited by
Visualization of brand positioning based on consumer web search information, Internet Research, 2017, 27, 2, 381  crossref(new windwow)
Y. Carrière-Swallow and F. Labbé, “Nowcasting with Google Trends in an Emerging Market,” Journal of Forecasting, vol. 32, pp. 289-298, 2013. crossref(new window)

L. W. a. E. Brynjolfsson, “The Future of Prediction: How Google Searches Fore-shadow Housing Prices and Sales,” presented at the NBER Conference Technological Progress & Produc-tivity Measurement, 2009.

J. Ginsberg, M. H. Mohebbi, R. S. Patel, L. Brammer, M. S. Smolinski, and L. Brilliant, “Detecting influenza epidemics using search engine query data,” Nature, vol. 457, pp. 1012-4, Feb 19 2009. crossref(new window)

W. W. Moe and P. S. Fader, “Dynamic Conversion Behavior at E-Commerce Sites,” Management Science, vol. 50, pp. 326-335, 2004. crossref(new window)

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.

P. T. M. a. E. M. Catherine Lui, “On the predictability of the U.S. Elections through Search Volume Activity,” in e-Society Conference, Avila, Spain.

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

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

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. crossref(new window)

N. Askitas and K. F. Zimmermann, “Google Econometrics and Unemployment Forecasting,” Applied Economics Quarterly, vol. 55, pp. 107-120, 2009/04/01 2009. crossref(new window)

T. Preis, H. S. Moat, and H. E. Stanley, “Quantifying trading behavior in financial markets using Google Trends,” Sci Rep, vol. 3, p. 1684, 2013. crossref(new window)

Google, How does Google Trends Work, Official Site, 2010. Available:

P. M. Polgreen, Y. Chen, D. M. Pennock, and F. D. Nelson, “Using internet searches for influenza surveillance,” Clin Infect Dis, vol. 47, pp. 1443-8, Dec 1 2008. crossref(new window)