Convert for loop to apply family function for better performance
Convert for loop to apply family function for better performance
Below I am forecasting for the next 30 days . If the input data is around 100k the for loop is extremely slow (takes about 2 hours) . the code using the for loop as below.
ns<-ncol(TS) # count number of columns to run the loop
output<-matrix(NA,nrow=30,ncol=ns)
for (i in 2:ns)
{
output[,i]<- forecast(auto.arima(TS[,i],allowmean = T,D=1),h=30 )$mean
i=i+1
}
I have tried using lapply as below but the run time remains the same.
lapply(TS, function(x) forecast(auto.arima(x,allowmean = T,D=1),h=30 ))
Is there an alternate function/method I can use to improve the performance?
The runtime for apply family and the explicit forloop are morelessly the same. Thereis no gain of efficiency when usingapply family. They arejust implicit for-loops
– Onyambu
Jul 3 at 9:38
That
i = i+1 does absolutely nothing.– LAP
Jul 3 at 9:38
i = i+1
In
for loops you don't need to put i = i+1– Emmanuel-Lin
Jul 3 at 9:38
for
i = i+1
try some parallel lapply
– s.brunel
Jul 3 at 9:43
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Please provide a reproducible example
– Emmanuel-Lin
Jul 3 at 9:37