choose_model <- function(x,h,end_train,start_test){
  library(forecast)
  library(forecastHybrid)
  library(tidyverse)

  #train data

  x_train <- window(x, end = end_train )

  x_test <- window(x, start = start_test)

  h1=length(x_test)

  #model1

  stlf(x_train,method="arima",s.window= 12, h=h1)-> fc_stlf

  #model2
  auto.arima(x_train, stepwise = FALSE, approximation = FALSE)%>%forecast(h=h1) -> fc_arima

  #model3
  set.seed(12345)#for nnetar model
  nnetar(x_train)%>%forecast(h=h1) -> fc_nnetar

  #model4
  snaive(x_train,h=h)->fc_snaive

  #model5
  hybridModel(x_train,models = "anst",weights = c("equal"),errorMethod = c("RMSE", "MAE", "MASE"),verbose=FALSE)%>%forecast(h=h1) -> fc_hy

  #model6
  hybridModel(x_train,models = "an",weights = c("equal"),errorMethod = c("RMSE", "MAE", "MASE"),verbose=FALSE)%>%forecast(h=h1) -> fc_hy_2

  #model7

  ets(x_train)%>%forecast(h=h1)->fc_ets

  #model8

  ses(x_train, h=h1)->fc_ses

  #model9

  holt(x_train, h=h1)->fc_holt

  #model10

  hw(x_train,seasonal = "additive",  h=h1)->fc_hw_ad

  #model11

  hw(x_train,seasonal = "multiplicative", h=h1)->fc_hw_mul

  #model12

  hw(x_train,seasonal = "additive",damped = TRUE,  h=h1)->fc_hw_ad_dam


  #model13
  hw(x_train,seasonal = "multiplicative",damped = TRUE, h=h1)->fc_hw_mul_dam

  #accuracy


  model1 <- accuracy(fc_stlf$mean,x_test)[2]

  model2 <- accuracy(fc_arima$mean,x_test)[2]

  model3 <- accuracy(fc_nnetar$mean,x_test)[2]

  model4 <- accuracy(fc_snaive$mean,x_test)[2]

  model5 <- accuracy(fc_hy$mean,x_test)[2]

  model6 <- accuracy(fc_hy_2$mean,x_test)[2]

  model7 <- accuracy(fc_ets$mean,x_test)[2]

  model8 <- accuracy(fc_ses$mean,x_test)[2]

  model9 <- accuracy(fc_holt$mean,x_test)[2]

  model10 <- accuracy(fc_hw_ad$mean,x_test)[2]

  model11 <- accuracy(fc_hw_mul$mean,x_test)[2]

  model12 <- accuracy(fc_hw_ad_dam$mean,x_test)[2]

  model13 <- accuracy(fc_hw_mul_dam$mean,x_test)[2]

  best_model <- min(c(model1,model2,model3,model4,model5,model6,model7,model8,model9,model10,model11,model12,model13))


  if(best_model==model1){

    return(stlf(x,method="arima",s.window= 20, h=h)$mean)

  }
  if(best_model==model2){

    return(forecast(auto.arima(x, stepwise = FALSE, approximation = FALSE),h=h )$mean)


  }
  if(best_model==model3){

    set.seed(12345)
    return(forecast(nnetar(x),h=h)$mean)


  }
  if(best_model==model4){

    return(snaive(x,h=h)$mean)

  }

  if(best_model==model5){

    return(forecast(hybridModel(x,models = "anst",weights = c("equal"),errorMethod = c("RMSE", "MAE", "MASE"),verbose=FALSE ),h=h)$mean)

  }

  if(best_model==model6){

    return(forecast(hybridModel(x,models = "an",weights = c("equal"),errorMethod = c("RMSE", "MAE", "MASE"),verbose=FALSE),h=h)$mean)

  }

  if(best_model==model7){

    return(forecast(ets(x),h=h)$mean)

  }

  if(best_model==model8){
    return(ses(x,h=h)$mean)
  }
  if(best_model==model9){
    return(holt(x, h=h)$mean)
  }

  if(best_model==model10){
    return(hw(x,seasonal = "additive",  h=h)$mean)
  }
  if(best_model==model11){
    return(hw(x,seasonal = "multiplicative", h=h)$mean)
  }
  if(best_model==model12){
    return(hw(x,seasonal = "additive",damped = TRUE,  h=h)$mean)
  }
  if(best_model==model13){
    return(hw(x,seasonal = "multiplicative",damped = TRUE, h=h)$mean)
  }
}


choose_model_monthly(my_data,7,c(2018,01),c(2018,02))