Two weeks into Google summer of code

As I have now completed two full weeks as a Google summer of code student, things have got so much better now. I have learned quite a lot about open source communities and how good software is written. Most importantly I have learned a lot about the Python programming language.

As I have mentioned in my previous blogs that my project is based on building up an Automatic Forecasting module for the Statsmodels package which would help in automatically setting up time series models. I have been able to successfully meet my targets for the same. During the first week, my objective was to complete a simple module that would take a given range of parameter for SARIMAX models and would select the best combinations of parameters (p, q, i.e., Autoregressive and moving average parameters respectively) based on AIC(Akaike Information criteria) values.

The target for my second week was to design the classes and the supporting functions that the end user would require to use the models. For this part, we had split our dataset into two sets, i.e., the training set(on which the models are built) and the testing set(on which the models are validated). A part of this also included calculating different accuracy measures like MAE(Mean Absolute Error), RMSE(Root Mean Squared Error), MAPE(Mean Absolute Percentage Error), etc. which would be used to check the accuracy of our models. These accuracy measures were performed on the testing set, and the above measures were used to validate the models.

All my commits are present in a single pull request, and the fork to which I am pushing my changes can be found here at

https://github.com/abhijeetpanda12/statsmodels/tree/auto-forecast-1

Please provide me any feedback that would help in doing better for my GSoC project and my blog here.

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