I have a data as follows

    Date	Paid
    Jan-14	13392905
    Feb-14	11939873
    Mar-14	12473667
    Apr-14	12237110
    May-14	12579693
    Jun-14	12030095
    Jul-14	12052101
    Aug-14	10205025
    Sep-14	12102526
    Oct-14	1237336
    Nov-14	12148331
    Dec-14	9842860
    Jan-15	11990085
    Feb-15	11061740
    Mar-15	12076397
    Apr-15	11702514
    May-15	11395657
    Jun-15	11817594
    Jul-15	11643682
    Aug-15	10243241
    Sep-15	12233001
    Oct-15	11769231
    Nov-15	12652418
    Dec-15	9774333
    Jan-16	11888965
    Feb-16	11892589
    Mar-16	11419517
    Apr-16	12143787
    May-16	12330387
    Jun-16	11929805
    Jul-16	11583281
    Aug-16	11995557
    Sep-16	12646047
    Oct-16	12677372
    Nov-16	13301244
    Dec-16	9915846
Using 2014-2015 information I want to generate forecasts until 2020.Hence, I have split the data into train & test

      data.train<-window(mydata_ts,start=c(2014,1),end=c(2015,12))
      data.test<-window(mydata_ts,start=c(2016,1))
      auto.arima(data.train,trace=TRUE,test="kpss",ic="aic")
& following are the results:

      Best model: ARIMA(0,0,0)            with non-zero mean 

      Series: data.train 
      ARIMA(0,0,0) with non-zero mean 

      Coefficients:
            mean
      11275058.9
      s.e.    463612.8

      sigma^2 estimated as 5.381e+12:  log likelihood=-385.31
      AIC=774.62   AICc=775.19   BIC=776.98
& I get flat forecasts.I have tried using drift but that only helps when forecasting for 2016 & flattens 2017 onward. Is there something that can be done to overcome this.I have also tried the similar exercise in SAS using proc UCM & that seems to generate forecasts better than the auto.arima.

Can someone help out?