ARIMA D coefficient for seasonality lesser than frequency Lets suppose we have a time series with monthly data (frequency=12)
my_tS <- ts(my_monthly_data, start=c(2002,1), frequency=12)

When I plot it, I can see a seasonality every 6 months. So, to remove it, I should compute a differencing every 6 months:
my_tS_stationary <- diff(my_tS, 6)

I check it with KPSS and its stationary. Now, I want to model it with ARIMA(p,d,q)(P,D,Q)[12]. Which value should I use for D? A value of 1 is for 12 months seasolnality (the frequency of my TS), but mine is every 6 months...
Edit: As requested, I Add the images od the graphs I made to check seasonality



EDIT AGAIN: Added season plot... Data are flights through year. Now, with season plot, I think it has a 12 months seasonality with two peaks in the year. Thanks, Stephan and Richard, for guiding me to find this!


The D parameter governs the number of seasonal differences, where "seasonal" is understood to be as per the underlying time series frequency. What you are implicitly doing is considering your time series as having a frequency not of 12 (monthly data with yearly seasonality), but of 6 (monthly data with half-yearly seasonality).
So the best solution would be to recode your time series with a new frequency=6 attribute, then feed it into auto.arima() (with or without a hard setting for the D parameter):
> library(forecast)
> auto.arima(ts(AirPassengers,frequency=6))
Series: ts(AirPassengers, frequency = 6) 
ARIMA(4,1,2) with drift 

Coefficients:
         ar1     ar2      ar3      ar4      ma1      ma2   drift
      0.2243  0.3689  -0.2567  -0.2391  -0.0971  -0.8519  2.6809
s.e.  0.1047  0.1147   0.0985   0.0919   0.0866   0.0877  0.1711

sigma^2 = 706.3:  log likelihood = -670.07
AIC=1356.15   AICc=1357.22   BIC=1379.85

(Removed erroneous images)
