Some people from this project came to Duke this week, touting the greatness of their new method for approximate Bayesian inference and their sweet new R package. Though they didn't actually tell us how it works (apparently it is based upon some sort of advanced computational wizardry), I was excited to try out this new toy. If you browse the website, a lot of the examples are either time series (time serial?) or spatial. Since I deal with spatial data all the effing time, I decided to find some time series data and give INLA a whirl.
So, I present to you the schmuck dataset!
This is the relative search volume of the word "schmuck" weekly from some time in 2004 until yesterday. You could get it yourself off of Google Trends (thanks, Google! ), or you could just get a version here, from which I've already removed all of the extraneous junk.
Look at this beauty!! Something is definitely happening consistently during the 2nd week of December that makes people reaaaally want to search for the word schmuck.
Back to the statistics... using INLA, I tried fitting a latent AR(1) term to this even though that is clearly not the right model. No dice. I tried adding a seasonal component. Still no. It keeps shooting me an error message without much of an explanation. Something about a singular matrix. What matrix, I don't know. So, that's it in a nutshell. Although I was super pumped to take INLA for a test drive, this sort of knocked the wind out of my sails. This is not to say it doesn't work, just that I can't get it to work on my new favorite data set.
So, I leave you with one more seasonal insult, losers. Below you will find the relative search volume for the term "loser."
What is it about the cold months that makes people really interested in schmucks and losers? 10 units of pride to anyone who can give me a plausible explanation!