please note that I am not a statistician (though I have some experience in machine learning). I want to model and predict trends in search behavior. To improve the predictive accuracy I want to use "similar trends" and learn from their behavior (from my research this seems to be called an exogenous input). This could be similar behavior at the same point in time (probably easier) but also points in the past. One example might be that the interest in Whitney Houston's death behaves similar to Michael Jackson's death a little bit before that. Is there any machinery that automatically finds these correspondences in time series and uses them? Maybe something that aligns peaks or uses correlation or dynamic time warping to align the sequences the right way for modeling/prediction.

Here are two more examples: http://www.google.com/trends/explore#q=%22madden%20nfl%2011%22%2C%20%22madden%20nfl%2012%22%2C%20%22madden%20nfl%2010%22%2C%20%22madden%20nfl%2013%22&cmpt=q One time series has little information about how the future might look like but similar trends (previous games) exhibit very similar behavior.

http://www.google.com/trends/explore#q=penn%20state%2C%20joe%20paterno%2C%20penn%20state%20football%2C%20penn%20state%20scandal%2C%20penn%20state%20football&date=1%2F2008%2060m&cmpt=q This is an example that is a little more tricky. Here some trends are somewhat independent over most of the time interval but at the time of the peak they are suddenly very related (football scandal and head coach). It would be great to have some way of automatically figuring out at the beginning of the peak that now I could really use the information of the other time series.

Any ideas and pointers are very welcome. Coding-wise I would prefer to use Python although it seems I might have to learn how to use R. Maybe I could also combine the two (ie. calling R methods from Python).

Thanks a lot!

Tim

I want to model and predict trends in search behavior. To improve the predictive accuracy I want to use "similar trends" and learn from their behavior (from my research this seems to be called an exogenous input). This could be similar behavior at the same point in time (probably easier) but also points in the past. One example might be that the interest in Whitney Houston's death behaves similar to Michael Jackson's death a little bit before that. Is there any machinery that automatically finds these correspondences in time series and uses them? Maybe something that aligns peaks or uses correlation or dynamic time warping to align the sequences the right way for modeling/prediction.

Here are two more examples:

• Google trends for Madden NFL games. One time series has little information about how the future might look like but similar trends (previous games) exhibit very similar behavior.
• Google trends for state football. This is an example that is a little more tricky. Here some trends are somewhat independent over most of the time interval but at the time of the peak they are suddenly very related (football scandal and head coach). It would be great to have some way of automatically figuring out at the beginning of the peak that now I could really use the information of the other time series.

Any ideas and pointers are very welcome. Coding-wise I would prefer to use Python although it seems I might have to learn how to use R. Maybe I could also combine the two (ie. calling R methods from Python).