some time ago the runtime of the forecast we are using doubled from one (monthly) execution to the next. So I've wrote a small test script to analyze how the execution time of some of the forecasting method performs with different length timeseries:
start <- 1
end <- 100
# FROM: https://github.com/robjhyndman/M4metalearning/blob/61ddc7101680e9df7219c359587d0b509d2b50d6/R/forec_methods_list.R#L40
auto_arima_forec <- function(x, h) {
model <- forecast::auto.arima(x, stepwise = FALSE, approximation = FALSE)
forecast::forecast(model, h = h)$mean
}
generate_random_ts <- function(a = 1:1000, b = 0, c = 7) {
sin(a * rnorm(1, b, c)) +
sin(a * rnorm(1, b^2, c^2)) +
sin(a * rnorm(1, b^3, c^3)) +
sin(a * rnorm(1, b^4, c^4)) +
seq(a) / runif(a, 1, 10)
}
set.seed(4711)
truex <- generate_random_ts()
#plot(truex, type = "l")
timeseries <- list()
for (i in start:end) {
x <- ts(head(truex, i), frequency = 12)
timeseries <- rlist::list.append(timeseries, x)
}
jobs <- lapply(
timeseries[1:length(timeseries)],
function(x) {
local({
x <- x
bquote(auto_arima_forec(.(x), 24))
})
}
)
names(jobs) <- paste0(
"Length ts: ",
stringr::str_pad(start:end, 3, pad = "0")
)
mbm <- microbenchmark::microbenchmark(list = jobs)
save(mbm, file = "mbm.RData")
options(microbenchmark.unit = "eps")
print(mbm)
options(microbenchmark.unit = "relative")
print(mbm)
plot <- ggplot2::autoplot(mbm)
print(plot)
Most of them just (more or less) linearly increase in runtime for longer timeseries, which is to be expected. But the auto.arima method seems to be exploding (4-5 times more execution time) when the timeseries becomes longer then 71 month (=6 Years).
Do you know why this is the case and is it possible to decrease the magnitude of this sudden jump?
In order to keep this Issue short I only posted code and image for the auto.arima case.
Hello,
some time ago the runtime of the forecast we are using doubled from one (monthly) execution to the next. So I've wrote a small test script to analyze how the execution time of some of the forecasting method performs with different length timeseries:
Most of them just (more or less) linearly increase in runtime for longer timeseries, which is to be expected. But the auto.arima method seems to be exploding (4-5 times more execution time) when the timeseries becomes longer then 71 month (=6 Years).
Do you know why this is the case and is it possible to decrease the magnitude of this sudden jump?
In order to keep this Issue short I only posted code and image for the auto.arima case.