Here is a recent Google correlate query:
http://www.google.com/trends/correlate/search?e=internet+usage&t=weekly#
As you can see in the search box at that link, I entered "internet usage" and Google did the rest. It shows a value of 0.9298 as the "correlation" with the query "data mining". However, when I read page 2 of the Google white paper [PDF], it says:
The objective of Google Correlate is to surface the queries in the database whose spatial or temporal pattern is most highly correlated with a target pattern. Google Correlate employs a novel approximate nearest neighbor (ANN) algorithm over millions of candidate queries in an online search tree to produce results similar to the batch-based approach employed by Google Flu Trends but in a fraction of a second. For additional details, please see the Methods section below....
So, my question is:
Is Google using a normal Pearson or Spearman correlation to find this stuff or are they using something else? If so, can you explain the general technique?
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Also, notice in the plot that the search for "internet usage" (and "data mining") drops during the summer months and really dives around Christmas. I would guess that kids and their homework have something to do with this.