Detailed Model Summary in na_kalman() about imputets HOT 2 OPEN

Yes, you are totally correct. It uses the default search parameters of auto.arima() from forecast.

So just run the following on your series:
(tsAirgap is just an example time series with NAs)

library("forecast")
auto.arima(tsAirgap)

Series: tsAirgap
ARIMA(0,1,1)(0,1,0)[12]

Coefficients:
ma1
-0.3745
s.e. 0.0918

sigma^2 estimated as 145.2: log likelihood=-466.04
AIC=936.09 AICc=936.18 BIC=941.84

This are then the ARIMA parameters for model='auto.arima'.

If you think there is a model that fits the time series better, you can also supply a ARIMA model you created:

# Example 5:  Perform imputation with KalmanSmooth and user created model
usermodel <- arima(tsAirgap, order = c(1, 0, 1))$model
na_kalman(tsAirgap, model = usermodel)

Thanks for your question 👍
Maybe it a good idea to think about providing more information with the output itself.
(to avoid this kind of workarounds) I'll keep this in mind for future versions.

from imputets.

Just as an addition:

Parameters from auto.arima will also be forwarded, if you supply them to na_kalman.

So you could also call:
na_kalman(tsAirgap, model ="auto.arima", seasonal = F)

You would get a different model then before.
seasonal is a parameter from forecast::auto.arima - which restricts to non-seasonal models when set false.

auto.arima(tsAirgap, seasonal = F)

ARIMA(0,1,4)

Coefficients:
ma1 ma2 ma3 ma4
0.3683 -0.1873 -0.2327 -0.5088
s.e. 0.0901 0.0992 0.0726 0.1025

sigma^2 estimated as 785.4: log likelihood=-625.33
AIC=1260.65 AICc=1261.09 BIC=1275.47

As you can see at the results, this now finds a different (in this case way worse model).

from imputets.