It’s almost one month since I officially started coding for Statsmodels as a part of Google Summer of Code. The journey till now has been very challenging and thrilling until now. The milestones which I cover every week has taught me a lot regarding code practice, statistics and open source. I am sharing a few of the work that I have done during the last two weeks which I feel were the most challenging milestones during my first month of contribution.
The third week of my code contribution was targetted at expanding my auto_order function(created during the first week) to support computing seasonality order and intercepts. This included developing the code to check for all the different possibilities of AR, MA parameters along with the seasonal parameters which would provide the least AIC for a particular input time-series.
The fourth week was focussed on building an auto_transformation module which would help in automatically transforming the time-series into a stationary time-series. Since statsmodels already includes the Box-Cox transformation functionality, my focus was to create a module which would predict the parameters for this transformation. The book by Draper and Smith – “Applied Regression Analysis” provided some useful techniques to do that. The parameters(lambda) for the Box-Cox transformation was predicted by checking the value of lambda that maximizes the likelihood of linear regression.
The functions a module that I have developed are now to be tested with real-life examples against other modules and package(like the forecast package in R).