# Sample Data
y <- c( -0.05628948,  0.01907727,  0.00000000, -0.01907727,  0.00000000, -0.01940678,
0.05724351, -0.01875946, -0.03848405,  0.05724351)
x <- c(0.000000000,-0.071700531 ,-0.023863364,  0.013701646,  0.000000000,  0.085009788,
  -0.028666940, -0.046181130, -0.027316528,  0.006895152)

#Fit the model
model <- arima(y, order=c(2,0,1),fixed = c(NA,NA,NA,NA,NA),xreg=x)

#Use the predict function with x again as the input
fore <- predict(model,newxreg = x)[1]

########################Output########################################
Model -
Call:
arima(y, order = c(2, 0, 1), xreg = x, fixed = c(NA, NA, NA, NA, NA))

Coefficients:
         ar1      ar2      ma1  intercept       x
      -0.7935  -0.5747  -0.2986    -0.0010  0.0569
s.e.   0.4327   0.4399   0.6892     0.0026  0.1245

sigma^2 estimated as 0.0005055:  log likelihood = 22.91,  aic = -33.83

Predict -

> fore
$`pred`
Time Series:
Start = 11
End = 20
Frequency = 1
[1] -0.03206240 -0.03614031 -0.03341961 -0.03128313 -0.03206240 -0.02722754
[7] -0.03369281 -0.03468892 -0.03361601 -0.03167025