The online documentation indicates that the algorithm under the hood is the same to estimate the (s)Arima models. During some tests, with a Kaggle dataset, I had different models: ARIMA function show me a sArima, auto.arima only Arima model.
auto.arima(tsbble_item1_store1$sales)
give
Best model: ARIMA(5,1,2)
and
tsbble_item1_store1 %>%
model(arima = ARIMA(sales))
give
# A mable: 1 x 2
# Key: store [1]
store arima
<dbl> <model>
1 1 <ARIMA(1,1,3)(0,0,2)[7]>
I have very different models. By the way, Arima's fable function shows me a better model, because it controls seasonality respect auto.arima function that doesn't, and the data show evident seasonality.
Does someone know the main differences in default parameters when the two functions try to estimate the model, because I didn't understand from docs?
Sorry if I had some mistakes
thank's in advance
Have nice day
MC