Google Trends is a valuable source of econnomic information. Previous studies have utilized Google Trends  data for economic forcasting . We expand this work by providing algorithms to combine and aggregate Trends data and show how time series clustering can be used to analyze competition .
Web search data are a valuable source of business and economic information. Previous studies have utilized Google Trends web search data for economic forecasting . We expand this work by providing algorithms to combine and aggregate search volume data, so that the resulting data is both consistent over time and consistent between data series. We give a brand equity example, where Google Trends is used to analyze shopping data for 100 top ranked brands and these data are used to nowcast economic variables. We describe the importance of out of sample prediction and show how principal component analysis (PCA) can be used to improve the signal to noise ratio and prevent overfitting in nowcasting models. We give a finance example, where exploratory data analysis and classification is used to analyze the relationship between Google Trends searches and stock prices .
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Google Trends is
Web search data are
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econnomic
business and economic
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web search
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forcasting
forecasting
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Trends data and show how time series clustering
search volume data, so that the resulting data is both consistent over time and consistent between data series. We give a brand equity example, where Google Trends is used to analyze shopping data for 100 top ranked brands and these data are used to nowcast economic variables. We describe the importance of out of sample prediction and show how principal component analysis (PCA)
R
analyze competition
improve the signal to noise ratio and prevent overfitting in nowcasting models. We give a finance example, where exploratory data analysis and classification is used to analyze the relationship between Google Trends searches and stock prices