Parallelize 'strucchange' functions

Henrik Bengtsson

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The futurize package allows you to easily turn sequential code into parallel code by piping the sequential code to the futurize() function. Easy!

TL;DR

library(futurize)
plan(multisession)
library(strucchange)

data("Nile")
bp.nile <- breakpoints(Nile ~ 1) |> futurize()

Introduction

The strucchange package provides the breakpoints() function for estimating one or more change points in a data trace, e.g. in time-series data.

Example: Finding breakpoints in time-series data

library(futurize)
plan(multisession)
library(strucchange)

## UK Seatbelt data: a SARIMA(1,0,0)(1,0,0)_12 model
## (fitted by OLS) is used and reveals (at least) two
## breakpoints - one in 1973 associated with the oil crisis and
## one in 1983 due to the introduction of compulsory
## wearing of seatbelts in the UK.
data("UKDriverDeaths")

seatbelt <- log10(UKDriverDeaths)
seatbelt <- cbind(seatbelt, lag(seatbelt, k = -1), lag(seatbelt, k = -12))
colnames(seatbelt) <- c("y", "ylag1", "ylag12")
seatbelt <- window(seatbelt, start = c(1970, 1), end = c(1984, 12))
plot(seatbelt[,"y"], ylab = expression(log[10](casualties)))

## testing
re.seat <- efp(y ~ ylag1 + ylag12, data = seatbelt, type = "RE")
plot(re.seat)

## dating
bp.seat <- breakpoints(y ~ ylag1 + ylag12, data = seatbelt, h = 0.1) |> futurize()
lines(bp.seat, breaks = 2)

This will parallelize the dynamic programming algorithm for computing the optimal breakpoints, given that we have set up parallel workers, e.g.

plan(multisession)

The built-in multisession backend parallelizes on your local computer and works on all operating systems. There are other parallel backends to choose from, including alternatives to parallelize locally as well as distributed across remote machines, e.g.

plan(future.mirai::mirai_multisession)

and

plan(future.batchtools::batchtools_slurm)

Supported Functions

The following strucchange functions are supported by futurize():

• breakpoints() for ‘formula’

Without futurize: Manual PSOCK cluster setup

For comparison, here is what it takes to parallelize breakpoints() using the parallel and doParallel packages directly, without futurize:

library(strucchange)
library(parallel)
library(doParallel)

data("Nile")

## Set up a PSOCK cluster and register it with foreach
ncpus <- 4L
cl <- makeCluster(ncpus)
registerDoParallel(cl)

## Find breakpoints in parallel via foreach
bp.nile <- breakpoints(Nile ~ 1, hpc = "foreach")

## Tear down the cluster
stopCluster(cl)
registerDoSEQ()  ## reset foreach to sequential

This requires you to manually create a cluster, register it with doParallel, and remember to tear it down and reset the foreach backend when done. If you forget to call stopCluster(), or if your code errors out before reaching it, you leak background R processes. You also have to decide upfront how many CPUs to use and what cluster type to use. Switching to another parallel backend, e.g. a Slurm cluster, would require a completely different setup. With futurize, all of this is handled for you - just pipe to futurize() and control the backend with plan().