Moving forward with Exponential Smoothing models

The first part of GSoC 2018 is over now with the completion of the First Evaluations, and I am really thankful to my mentor for passing me in it.

I had planned my project in an organized manner while writing the proposal and I am happy to be on track now with a few remaining parts. The first month, according to the plan, was to focus on the SARIMAX model and their model selection while the second month, which is now, is to focus on the Exponential Smoothing models and their model selection. Last week has been spent in deciding the parameters that would be valuable to keep while selecting an ES model for automatically forecasting the time-series data.

To get into the details, I have referred to the ets function of the forecast package in R and had run a few unit tests to check if the results match with them. Furthermore, we are following a brute force approach to fit the Exponential Smoothing models and running a check for their in-sample Information Criteria like AIC to choose the best model. The best model is then returned to use it for the forecasts.

Apart from these, One of my tasks is to figure out a way to connect the various modules and classes, that I have built during the first month, with the ES modules so that they all work together.

I’ll keep on posting more on this project after I complete the module and make it a really functional project.

Leave a Reply

Your email address will not be published. Required fields are marked *