control <- trainControl(method = "timeslice", initialWindow = 225,  fixedWindow = TRUE, horizon = 1)
mynn <- train(mytsframe4[,c(2:3)], mytsframe4[,1], method = "mlp", size = 2,
          metric = c("RMSE"), maximize = FALSE, trControl = control)
mynn

Multi-Layer Perceptron

236 samples
  2 predictor

No pre-processing
Resampling: Rolling Forecasting Origin Resampling (1 held-out with a fixed window)
Summary of sample sizes: 225, 225, 225, 225, 225, 225, ...
Resampling results across tuning parameters:

  size  RMSE           Rsquared  RMSE SD
  1     0.05837386693  NaN       0.04002651320
  3     0.05759843218  NaN       0.04774038998
  5     0.07597407274  NaN       0.03000920417