R version 3.0.2 (2013-09-25) -- "Frisbee Sailing"
Copyright (C) 2013 The R Foundation for Statistical Computing
Platform: i686-pc-linux-gnu (32-bit)

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> x <- c(0.7869,0.7439,0.7492,0.7804,0.7678,0.7573,0.7337,0.7136,0.7107,0.7015,0.6874,0.6754,0.6713,0.6849,0.7003,0.7309,0.7364,0.7439,0.7928,0.8188,0.784,0.7746,0.7677,0.7197,0.7304,0.7567,0.749,0.7328,0.7142,0.6927,0.6974,0.6953,0.699,0.6971,0.7246,0.7301,0.736,0.7585,0.7756,0.7564,0.7568,0.7593,0.779,0.7978,0.8125,0.8075,0.7781,0.771,0.7796,0.763,0.7531,0.7473,0.7707,0.7684,0.7702,0.759,0.7649,0.7508,0.7494,0.7334)
> par10 = 'FALSE'
> par9 = '1'
> par8 = '1'
> par7 = '1'
> par6 = '0'
> par5 = '12'
> par4 = '0'
> par3 = '1'
> par2 = '0.0'
> par1 = '0'
> par10 <- 'FALSE'
> par9 <- '1'
> par8 <- '1'
> par7 <- '1'
> par6 <- '0'
> par5 <- '12'
> par4 <- '0'
> par3 <- '1'
> par2 <- '0.0'
> par1 <- '0'
> #'GNU S' R Code compiled by R2WASP v. 1.2.327 ()
> #Author: root
> #To cite this work: Wessa P., (2013), ARIMA Forecasting (v1.0.9) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_arimaforecasting.wasp/
> #Source of accompanying publication: 
> #
> par1 <- as.numeric(par1) #cut off periods
> par2 <- as.numeric(par2) #lambda
> par3 <- as.numeric(par3) #degree of non-seasonal differencing
> par4 <- as.numeric(par4) #degree of seasonal differencing
> par5 <- as.numeric(par5) #seasonal period
> par6 <- as.numeric(par6) #p
> par7 <- as.numeric(par7) #q
> par8 <- as.numeric(par8) #P
> par9 <- as.numeric(par9) #Q
> if (par10 == 'TRUE') par10 <- TRUE
> if (par10 == 'FALSE') par10 <- FALSE
> if (par2 == 0) x <- log(x)
> if (par2 != 0) x <- x^par2
> lx <- length(x)
> first <- lx - 2*par1
> nx <- lx - par1
> nx1 <- nx + 1
> fx <- lx - nx
> if (fx < 1) {
+ fx <- par5
+ nx1 <- lx + fx - 1
+ first <- lx - 2*fx
+ }
> first <- 1
> if (fx < 3) fx <- round(lx/10,0)
> (arima.out <- arima(x[1:nx], order=c(par6,par3,par7), seasonal=list(order=c(par8,par4,par9), period=par5), include.mean=par10, method='ML'))

Call:
arima(x = x[1:nx], order = c(par6, par3, par7), seasonal = list(order = c(par8, 
    par4, par9), period = par5), include.mean = par10, method = "ML")

Coefficients:
         ma1    sar1     sma1
      0.3593  0.8502  -1.0000
s.e.  0.1407  0.1796   0.4194

sigma^2 estimated as 0.0005086:  log likelihood = 137.93,  aic = -267.86
> (forecast <- predict(arima.out,par1))
Error in ts(z[[1L]] + xm, start = xtsp[2L] + deltat(rsd), frequency = xtsp[3L]) : 
  'ts' object must have one or more observations
Calls: predict -> predict.Arima -> ts
Execution halted