Closed studsttat closed 2 months ago
It made me think of it since there is a python package named statsforecast
, where it has a function named auto_arima()
. The difference between auto.arima()
and auto_arima()
in statsforecast
is that the auto_arima()
function in statsforecast
is 1.5x faster than auto.arima()
(according to the GitHub page of statsforecast
).
And so, I thought it can be done with the use of parallel computing.
statsforecast
achieves its speed using JIT computing through Numba, a Python compiler. There is no such facility available for R. This has nothing to do with parallel computing, which is already available in both the Python and R implementations.
Nevermind, the auto.arima()
function can achieve such speed. I tried fitting ARIMA in 50.000 obs. using auto.arima()
, and the ARIMA was fitted in just an average of 3 seconds.
What makes you think this is possible? Any ideas how?