Avoid "optimization failure" in for loop in R - r

Question

I'm trying to make a lot of time series forecast using the HoltWinters function in R.
For this purpose, I use a for loop and inside I call to the function, and save the prediction in a data.frame.
The problem is that some results of the HoltWinters function gives errors, specifically optimization errors:
Error en HoltWinters(TS[[i]]) : optimization failure
This error break the loop.
So what I need is something like "try": if it can make the HoltWinters function, it saves the prediction, otherwise it save the error.
The code below replicates the problem:
data <- list()
data[[1]] <- rnorm(36)
data[[2]] <-
c(
24,24,28,24,28,22,18,20,19,22,28,28,28,26,24,
20,24,20,18,17,21,21,21,28,26,32,26,22,20,20,
20,22,24,24,20,26
)
data[[3]] <- rnorm(36)
TS <- list()
Outputs <- list()
for (i in 1:3) {
TS[[i]] <- ts(data[[i]], start = 1, frequency = 12)
Function <- HoltWinters(TS[[i]])
TSpredict <- predict(Function, n.ahead = 1)[1]
Outputs[[i]] <-
data.frame(LastReal = TS[[i]][length(TS[[i]])], Forecast = TSpredict)
}
Where i <- 2 The problem is generated.
What I need is that in this example the "Outputs" list is as follows:
> Outputs
[[1]]
LastReal Forecast
1 0.5657129 -2.274507
[[2]]
LastReal Forecast
1 error error
[[3]]
LastReal Forecast
1 0.4039783 -0.9556881
Thanks in advance.

Answer 1

I ran into this same problem with HoltWinters the other day and took Roman's advice by using tryCatch. It's not the most intuitive to implement based on the documentation, but I found this link very helpful for understanding it: How to write trycatch in R
My solution built off of the sample there.
data <- list()
data[[1]] <- rnorm(36)
data[[2]] <- c(
24,24,28,24,28,22,18,20,19,22,28,28,
28,26,24,20,24,20,18,17,21,21,21,28,
26,32,26,22,20,20,20,22,24,24,20,26
)
data[[3]] <- rnorm(36)
TS <- list()
Outputs <- list()
result <- list()
for (i in 1:3) {
Outputs[[i]] <- tryCatch({
#You can enter messages to see where the loop is
#message(paste("Computing", i))
TS[[i]] <- ts(data[[i]], start = 1, frequency = 12)
Function <- HoltWinters(TS[[i]])
TSpredict <- predict(Function, n.ahead = 1)[1]
result[[i]] <-
data.frame(LastReal = TS[[i]][length(TS[[i]])], Forecast = TSpredict)
},
error = function(cond) {
#message(paste("ERROR: Cannot process for time series:", i))
msg <- data.frame(LastReal = "error", Forecast = "error")
return(msg)
})
}
And for the Outputs
> Outputs
[[1]]
LastReal Forecast
1 0.4733632 0.5469373
[[2]]
LastReal Forecast
1 error error
[[3]]
LastReal Forecast
1 0.8984626 -0.5168826
You can use other error handling parameters such as finally and warning to deal with other exceptions that may arise.