Avoid "optimization failure" for loop in R
I am trying to make a lot of time series predictions using the HoltWinters function in R. For this purpose I am using a for loop and internally I call the function and store the prediction in data.frame.
The problem is that some of the results of the HoltWinters function give errors, in particular optimization errors:
Error en HoltWinters(TS[[i]]) : optimization failure
This error aborts the loop.
So I need something like "try": if it can make the HoltWinters function, it stores the prediction, otherwise it stores the error.
The code below replicates the issue:
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 am <- 2 The problem is given.
I need the Outputs list in this example to be 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.
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I ran into this problem the other day with HoltWinters and took roman advice using tryCatch
. This is not the most intuitive implementation based on the documentation, but I found this link very helpful in understanding it: How to write a trycatch in R
My solution is sample-based.
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 exits
> 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 options such as finally
and warning
to deal with other exceptions that may be thrown.
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