Before revision:  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 .
After revision:  We expand this work by providing algorithms to combine and aggregate Trends data and show how time series clustering 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 .