Benchmarking time series models

This is a quick post on the importance of benchmarking time-series forecasts.  First we need to reload the functions from my last few posts on times-series cross-validation.  (I copied the relevant code at the bottom of this post so you don't have to find it).

Next, we need to load data for the S&P 500.  To simplify things, and allow us to explore seasonality effects, I'm going to load monthly data, back to 1980.

Time series cross-validation 3

I've updated my time-series cross validation algorithm to fix some bugs and allow for a possible xreg term.     This allows for cross-validation of multivariate models, so long as they are specified as a function with the following paramters: x (the series to model), xreg (independent variables, optional), newxreg (xregs for the forecast), and h (the number of periods to forecast).  Note that h should equal the number of rows in the xreg matrix.  Also note that you need to forecast the xreg object BEFORE forecasting your x object.  For example, if you wish to forecast 12 months into the future, your xreg object should have 12 extra rows.

A pure R poker hand evaluator

There's already a lot of great posts out there about poker hand evaluators, so I'll keep this short.  Kenneth J. Shackleton recently released a very slick 5-card and 7-card poker hand evaluator called SpecialK.  This evaluator is licensed under GPL 3, and is described in detail in 2 blog posts: part 1 and part 2.  Since the provided code is open source, I felt free to hack around with it a bit, and ported the python source to R.