I have been following this discussion thread. A time series analysis package for Julia would be an excellent addition to the Julia community!
As I have it understood, there are packages in Julia, like TimeModels.jl under JuliaStats, but nothing specific to time series, as you are doing, @fipelle.
Thank you, and I am very much looking forward to this .
@fipelle Is there any way you could provide an âauto.arimaâ-like function, as can be found in R?
I did not like how after running an MCMC on my differenced time series data, I wasnât getting the results I was expecting. Sometimes, the âstandard errorâ doesnât apply, in which case, I can always try a Studentâs T random variable, with more degrees of freedom, yetâŚ
Itâs tough getting the right p, d, q parameters. I have already plotted the correlograms and found some seasonal behavior in the data. Should I try a periodogram also? For reference, I was following this online guide: https://towardsdatascience.com/arima-models-with-turing-jl-81dcf2a1094c.
Sure. There are different ways to do it. For instance, you could use the techniques I described in my latest paper. I think I will implement something similar and based on implementations in https://github.com/fipelle/ElasticNetVAR.jl. I will also try to add a version of the Politis and Romanoâs stationary bootstrap.
I am afraid it wonât be in the next release. As you can see in the dev branch I am currently working on implementing the VARIMA. I will do it, once this is in production. Would you mind adding an issue on the git page?
Before getting into fractional methods I think it would be a good idea to implement state-space approaches to model seasonality and other features in the data (jointly with the VARIMA). I did not see many packages around that allow for these hybrids.