问如何为R中的HoltWinters添加预测精度？

EN

library(forecast)
date = seq(as.Date("2019/01/01"), by = "month", length.out = 48)

productB = rep("B",48)
productB = rep("B",48)
productA = rep("A",48)
productA = rep("A",48)

subproducts1=rep("1",48)
subproducts2=rep("2",48)
subproductsx=rep("x",48)
subproductsy=rep("y",48)

b1 <- c(rnorm(30,5), rep(0,18))
b2 <- c(rnorm(30,5), rep(0,18))
b3 <-c(rnorm(30,5), rep(0,18))
b4 <- c(rnorm(30,5), rep(0,18))

dfone <- data.frame("date"= rep(date,4),
            "product"= c(rep(productB,2),rep(productA,2)),
            "subproduct"= 
c(subproducts1,subproducts2,subproductsx,subproductsy),
            "actuals"= c(b1,b2,b3,b4))

export_df <- split(dfone[1:4], dfone[3])

dummy_list <- split(dfone[1:4], dfone[3]) %>% lapply( function(x) 
x[(names(x) %in% c("date", "actuals"))])
dummy_list <-  lapply(dummy_list, function(x) { x["date"] <- NULL; x })


list_dfs <- list()
for (i in 1:length(unique(dfone$subproduct))) {
  #assign(paste0("df", i), as.data.frame(dummy_list[[i]]))
  list_dfs <-append(list_dfs,dummy_list[[i]])
}

combined_dfs <- Reduce(function(x, y) merge(x, y, all = TRUE,  
by='date'), list(list_dfs))

list_ts <- lapply(list_dfs, function(t) 
ts(t,start=c(2019,1),end=c(2021,6), frequency = 12)) %>%
  lapply( function(t) ts_split(t,sample.out=(0.2*length(t))))    # 
creates my train test split
list_ts <- do.call("rbind", list_ts)  #Creates a list of time series

n1 <- seq(0.1, 0.99, by = 0.1)
n2 <- seq(0.1, 0.99, by = 0.1)
n3 <- seq(0.1, 0.99, by = 0.1)

dat_n <- expand.grid(n1 = n1, n2= n2, n3 = n3) 
out<- lapply(seq_len(nrow(dat_n)), function(i) {
   c_triple_holtwinters_multiplicative <- lapply(list_ts[1: 
(length(list_ts)/2)], function(x) 
       forecast::forecast(HoltWinters(x,seasonal = "additive",alpha = 
dat_n$n1[i],beta=dat_n$n2[i],gamma=dat_n$n3[i])))
    c_triple_holtwinters_multiplicative <- 
 lapply(c_triple_holtwinters_multiplicative, "[", "mean")
  assign(paste0("c_triple_holtwinters_multiplicative", i), 
c_triple_holtwinters_multiplicative, envir = .GlobalEnv)
 c_triple_holtwinters_multiplicative})

 forecast::accuracy(forecast::forecast(HoltWinters(list_ts[[4]],
 seasonal="multiplicative",alpha=.1,beta=.1,gamma=.2),h=24),list_ts[[8]])

        ME     RMSE      MAE         MPE      MAPE      MASE        ACF1 Theil's U
Training set    86.77923 2325.705 1476.658   -5.382147  32.47896 0.5611823 -0.05022049        
 NA
Test set     -3165.29871 6126.887 5389.800 -102.314548 129.32404 2.0483154  0.33876651  
 2.446896

 c_triple_holtwinters_multiplicative <- 
     lapply(c_triple_holtwinters_multiplicative, "[", "mean")

forecast::accuracy(c_triple_holtwinters_multiplicative1[[1]],
 list_ts[[5]])[4] # pulls out the RMSE

dat_n <- expand.grid(n1 = n1, n2= n2, n3 = n3) 
out<- lapply(seq_len(nrow(dat_n)), function(i) {
  c_triple_holtwinters_additive <- lapply(list_ts[1: 
(length(list_ts)/2)], function(x) 
      forecast::forecast(HoltWinters(x,seasonal = "additive",alpha = 
dat_n$n1[i],beta=dat_n$n2[i],gamma=dat_n$n3[i])))
 #    c_triple_holtwinters_additive <- 
 # lapply(c_triple_holtwinters_additive, "[", "mean")
 assign(paste0("c_triple_holtwinters_additive", i), 
c_triple_holtwinters_additive, envir = .GlobalEnv)
 c_triple_holtwinters_additive})

forecast::accuracy(c_triple_holtwinters_additive1[[1]],list_ts[[5]])[4]