[mlpack] A potential project idea for GSOC 2021

[Marcus Edel]

Hello Rishabh,

thanks for reaching out and welcome to the community, I like the idea,
but we should check how mlpack can differentiate from the existing methods;
is there a recent method that is not available in other frameworks (check for
papers), can we make an existing method faster etc. As you said there are
frameworks out there that implemented the methods already, so I think it's a
good idea to check what mlpack can bring to the table.

Thanks,
Marcus

> On 10. Mar 2021, at 10:27, RISHABH GARG <rishabhgarg108 at gmail.com> wrote:
> 
> 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) -
> Naive
> Seasonal naive
> Seasonal trend loess decomposition
> Holt winters
> Exponential smoothing
> Arima
> 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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