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On the predictability of Search Trends

Monday, August 17, 2009

Posted by Yossi Matias, Niv Efron, and Yair Shimshoni, Google Labs, Israel.launchingskibasketballyear-over-yearcategoriesFood & DrinkAutomotiveobamatwitterandroidglobal warmingNews & Current EventsFlu Trends

How many search queries have trends that are predictable?

Are some categories more predictable than others? How is the distribution of predictable trends between the various categories?

How predictable are the trends of aggregated search queries for different categories? Which categories are more predictable and which are less so?

a posteriorittttpredictableOn the Predictability of Search Trends

Over half of the most popular Google search queries are predictable in a 12 month ahead forecast, with a mean absolute prediction error of about 12%.

Nearly half of the most popular queries are not predictable (with respect to the model we have used).

Some categories have particularly high fraction of predictable queries; for instance, Health (74%), Food & Drink (67%) and Travel (65%).

Some categories have particularly low fraction of predictable queries; for instance, Entertainment (35%) and Social Networks & Online Communities (27%).

The trends of aggregated queries per categories are much more predictable: 88% of the aggregated category search trends of over 600 categories in Insights for Search are predictable, with a mean absolute prediction error of of less than 6%.

There is a clear association between the existence of seasonality patterns and higher predictability, as well as an association between high levels of outliers and lower predictability. For the Entertainment category that has typically less seasonal search behavior as well as relatively higher number of singular spikes of interest, we have seen a predictability of 35%, where as the category of Travel with a very seasonal behavior and lower tendency for short spikes of interest had a predictability of 65%.

One should expect the actual search trends to deviate from forecast for many predictable queries, due to possible events and dynamic circumstances.

We show the forecasting vs actual for trends of a few categories, including some that were used recently for predicting the present of various economic indicators. This demonstrates how forecasting can serve as a good baseline for identifying interesting deviations in actual search traffic.