CODEWITHSUNDEEP
performanceparallel-processingarima
Mark
Mark

Reputation: 1769

A fast solution to obtain the best ARIMA model in R (function `auto.arima`)

I have a data series composed by 2775 elements:

mean(series)
[1] 21.24862
length(series)
[1] 2775
max(series)
[1] 81.22
min(series)
[1] 9.192

I would like to obtain the best ARIMA model by using function auto.arima of package forecast:

library(forecast)
fit=auto.arima(Netherlands,stepwise=F,approximation = F)

But I am having a big problem: RStudio is running for an hour and a half without results. (I developed an R code to perform these calculations, employed on a Windows machine equipped with a 2.80GHz Intel(R) Core(TM) i7 CPU and 16.0 GB RAM.) I suspect that this is due to the length of time series. A solution could be the parallelization? (But I don't know how apply it).

Anyway, suggestions to speed this code? Thanks!

Upvotes: 2

Views: 2534

Answers (1)

DanWaters
DanWaters

Reputation: 536

The forecast package has many of its functions built with parallel processing in mind. One of the arguments of the auto.arima() function is 'parallel'.

According to the package documentation, "If [parallel = ] TRUE and stepwise = FALSE, then the specification search is done in parallel.This can give a significant speedup on mutlicore machines."

If parallel = TRUE, it will automatically select how many 'cores' to use (for a laptop or desktop, it is often the number of cores * 2. For example, I have 4 cores and each core has 2 processors = 8 'cores'). If you want to manually set the number of cores, also use the argument num.cores.

I'd recommend checking out the e-book written by Hyndman all about the package. It is like a time-series forecasting bible.

Upvotes: 1

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