Dear Stan users,
Me and my supervisor currently have a project that involves forecasting of time series. The process is quite simple, it’s a univariate time series of interest spread, which the noise follows ARMA(1,2) model.
I have a very quick question regarding the technical details behind brms time series. We have gone through a paper from Chib & Greenberg about the time series and state-space model for ARMA(p,q) model, and derived mathematically the whole error/noise to follow a state specification of ARMA(1,2).
The current model we have for simple one is:
y_t = mu_0 + e_t
e_t = ar1 + ma1 + ma2 + u_t
where both e_t and u_t follow normal/gaussian process. In other words, this is just a LGSSM model.
Hence, I tried to seek some packages that might help in modeling my time series, and apparently, your package brms allow time series ARMA modeling in a very clean and easy way! However, to clarify, my analysis requires to use of state-space specification that follows ARMA(1,2). I am putting these on the forum to clarify whether brms package follows Kalman Filter smoothing, filtering, etc in time series to fit the ARMA(1,2) model?
I had a quick read about the CRAN pdf, and I cannot find anything about state-space specifications or Kalman Filter. Hence, I just wanted to clarify this!