[gsoc] Statsample | Getting started!

[ankur...@gmail.com]

Hi everyone!

I am currently reading more about ARIMA(Autoregressive Integrated Moving Average) model which is the classical which helps in classical time-series operation and hysterics. Estimation methods such as maximum likelihood estimation and least squares will be a great addon!

After some more reading; shortly, I will take some time to explore their implementation in The R Project and Statsmodels., especially, R seems to have substantial packages for ARIMA, it's forecast model and other components. It will be fun!

As soon as I get more lead, I can't wait to get started with their implementation in the first phase of my coding period! I think after covering up the pending tests and some documentation for Statsample, the curve for development will turn out to be more smooth; making me more familiar with the available modules and extensions.

Help, continuous feedback and inspiration from SciRuby community is a great catalyst; Please do feel free to guide me during any point with your opinions, guidance, reviews and research etc. I will be very grateful! As always, I can be instantly reached at ankurgel [at] gmail [dot] com, and `ankurgel` on Skype!


Regards
- Ankur Goel

[John Woods]

Hi Ankur,

I'm curious as to how you plan the interface to look for ML. Can you talk a little about that?

Thanks!

John

--
You received this message because you are subscribed to the Google Groups "SciRuby Development" group.
To unsubscribe from this group and stop receiving emails from it, send an email to sciruby-dev...@googlegroups.com.
For more options, visit https://groups.google.com/groups/opt_out.
 
 

[ankur...@gmail.com]

Hi
As in case of R, at a basic front, it provides method arima() in Timeseries package. arima() is equipped with certain arguments to give out estimation, as:

• timeseries
• order - p(autoregressive parameter), d(differenced parameter), q(moving parameter)
• transforms
• estimation method - for MLE, to maximize the likelihood over the series, we can provide it default parameter to determine maximum likelihood with kalman filtering, other estimation techniques can be used are conditional and unconditional least squares estimation.
  

It will be better to extend a new module Arima from TimeSeries and implement it's API there. On TimeSeries object, we should be able to call arima with desired estimation and order to give out resultant estimation :) 

Regards

[John Woods]

You've described the R interface. But R interfaces are often really terrible. How can it be improved and made more Ruby-like?