d<-read.csv("MYFILE", header = TRUE, sep =";")
t<-as.Date(d$date,format="%d.%m.%Y")
require("tseries")
require("forecast")
 DJCT<-d$DJCT
 DJCTaprx<-na.approx(DJCT)
 DJCTretINSA<-ln(DJCTaprx[368:1828]/lead(DJCTaprx[368:1828], 1))[1:1460]
 DJCTretOUTSA<-ln(DJCTaprx[3:368]/lead(DJCTaprx[3:368], 1))
 DJCTret<-ln(DJCTaprx/lead(DJCTaprx, 1))
auto.arima(DJCTretINSA, stationary=TRUE)
Series: DJCTretINSA
ARIMA(1,0,1) with zero mean

Coefficients:
         ar1     ma1
      0.4807  -0.350
s.e.  0.1449   0.155

sigma^2 estimated as 7.947e-05:  log likelihood=4820.64
AIC=-9635.29   AICc=-9635.27   BIC=-9619.43
 arimaDJCTinsa<-auto.arima(DJCTretINSA, stationary=TRUE)

 plot(forecast(arimaDJCTinsa, h=365))

accuracy(forecast(arimaDJCTinsa, h=365), DJCTretOUTSA[1:365])
                        ME        RMSE         MAE  MPE MAPE      MASE
Training set -0.0001304150 0.008908426 0.006162879  NaN  Inf 0.8182688
Test set      0.0003074467 0.010278043 0.006968379 -Inf  Inf 0.9252182
                    ACF1
Training set 0.002305611
Test set              NA

tsDJCT<-ts(ln(DJCTaprx[368:1828]/lead(DJCTaprx[368:1828], 1))[1:1460], frequency=365)
bestfit <- list(aicc=Inf)
  for(i in 1:25)
  {
  fit <- auto.arima(tsDJCT, xreg=fourier(tsDJCT, K=i), seasonal=FALSE)
  if(fit$aicc < bestfit$aicc)
   bestfit <- fit
   else break;
   print(i)
   }
[1] 1

auto.arima(tsDJCT, xreg=fourier(tsDJCT, K=1), seasonal=FALSE)
Series: tsDJCT
Regression with ARIMA(1,0,1) errors

Coefficients:
         ar1      ma1  S1-365  C1-365
      0.4519  -0.3228  -7e-04  -3e-04
s.e.  0.1538   0.1633   4e-04   4e-04

sigma^2 estimated as 7.939e-05:  log likelihood=4822.33
AIC=-9634.67   AICc=-9634.63   BIC=-9608.24
fcDJCT<-forecast(auto.arima(tsDJCT, xreg=fourier(tsDJCT, K=1), seasonal=FALSE),xreg=fourier(tsDJCT, K=1), h=365)

accuracy(fcDJCT, DJCTretOUTSA[1:365])
                        ME        RMSE         MAE MPE MAPE      MASE
Training set -0.0001320885 0.008898123 0.006158973 Inf  Inf 0.8177503
Test set      0.0003072622 0.010246078 0.006997517 NaN  Inf 0.9290870
                    ACF1
Training set 0.001586422
Test set              NA