[mlpack] A potential project idea for GSOC 2021

[RISHABH GARG]

Hello everyone,

As most of us know that time series analysis and forecasting methods are
quite useful in the real world. In most of the practical life datasets, we
see some or many time dependent features. Thus, they are highly useful and
powerful methods. Therefore, in my opinion every machine learning / data
science library should have these methods. But unfortunately, mlpack does
not have any time series method implemented yet :(

Therefore I would like to propose this as a project idea for GSOC 2021 of
implementing time series forecasting models. Some of the most famous and
commonly used forecasting methods are listed below (mostly taken from issue
#2668) -

   1. Naive
   2. Seasonal naive
   3. Seasonal trend loess decomposition
   4. Holt winters
   5. Exponential smoothing
   6. Arima
   7. Autoregression


Over the limited time of GSOC 2021, it might not be possible to implement
all of these, so I can pick 2-3 methods from this list and implement them.
Also, these methods will require some basic utilities for their
implementations so that would also come under the hood of this project.


This would be a really interesting project for me to work on. I have
recently done a Data Science course in my university where I came across a
couple of these and I was fascinated at how useful these methods can be in
real life. I have already done some work implementing the Naive model in
#2789 and I would love to continue it over the coming summer.


I request all mentors to see if this could be a nice GSOC project and if
anyone like to mentor this project.


The valuable feedback of anyone from the mlpack community will be immensely
helpful.


Thanks and regards,

Rishabh Garg
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