ARFIMA model and accurancy function

I am foresting with data sets from fpp2 package and forecast package. So my intention is to make automatic forecasting with a several time series. So for that reason I am forecasting with function. You can see code below:

#  CODE
library(fpp2)
library(dplyr)
library(forecast)

df<-qauselec

# Forecasting function         
fct_fun <- function(Z, hrz = forecast_horizon) {
  timeseries <- msts(Z, start = 1956, seasonal.periods = 4)
  forecast <- arfima(timeseries)
}  
acc_list <- lapply(X = df, fct_fun)

So next step is to check accuracy of model. So for that reason I am trying with this line of code you can see below

accurancy_arfima <- lapply(acc_list,  accuracy)

Until now this line of code or function accuracy worked perfectly with other models like snaive,ets etc. but with arfima can’t work properly. So can anybody help me how to resolve this problem with accuracy function?

Solution

Follow R-documentation, Returns range of summary measures of the forecast accuracy. If x is provided, the function measures test set forecast accuracy based on x-f . If x is not provided, the function only produces training set accuracy measures of the forecasts based on f["x"]-fitted(f). And usage summary can be seen :

accuracy(f, x, test = NULL, d = NULL, D = NULL,
  ...)

So :

accuracy(acc_list[[1]]$fitted, df)

If you want to evaluate separately accuracy, It will work.

a <- c() 
for (i in 1:4) {
  b <- accuracy(df[i], acc_list[[1]]$fitted[i])
  a <- rbind(a,b)
}