DataFVM <- read.csv("AM1.csv", header=TRUE,na.strings=c("NULL",""))

Data <- subset(DataFVM,select=c(ID,Backlog))
Data <- Data[(Data$ID %in% c('905')),]
backlog <- as.vector(Data$Backlog)
backlog <- as.ts(backlog)

bestfit <- list(aicc=Inf)
for(i in 1:25)
{
  fit <- auto.arima(backlog, xreg=fourier(backlog, K=i), seasonal=FALSE)
  if(fit$aicc < bestfit$aicc)
    bestfit <- fit
  else break;
}
fc <- forecast(bestfit, xreg=fourierf(backlog, K=1, h=104))

ID  Backlog
905 0.99
905 0.96
905 0.98
905 0.87
905 0.95
905 0.91
905 0.96
905 0.92
905 0.9
905 0.91
905 0.96
905 0.95
905 0.87
905 0.99
905 0.95
905 0.99
905 0.93
905 0.94
905 0.96
905 0.98
905 0.71
905 0.84
905 0.86
905 0.92
905 0.91
905 1
905 0.96
905 0.92
905 0.96
905 0.92
905 0.83
905 0.93
905 0.97
905 0.67
905 0.89
905 0.92
905 0.95
905 0.94
905 0.95
905 1
905 0.98
905 0.94
905 0.88

#backlog <- as.ts(backlog)
backlog <- as.ts(backlog,frequency=7)