Implements a method for high-dimensional time series segmentation under a piecewise stationary factor-adjusted vector autoregressive model. See
High-dimensional time series segmentation via factor-adjusted vector autoregressive modelling
by Haeran Cho, Hyeyoung Maeng, Idris A. Eckley and Paul Fearnhead. See arXiv:2204.02724 for full details.
To install fvarseg from GitHub:
devtools::install_github("https://github.com/haeran-cho/fvarseg")
We can generate an example dataset used in the above paper for simulation studies as
out <- sim.data(n = 2000, p = 100, q = 2, d = 1,
cp.common = 1:3/4, den.common = .5, type.common = 'ma',
cp.idio = c(3, 5)/8, seed = 123)
x <- out$x
Apply fvar.seg with default settings.
fs <- fvar.seg(x, q = NULL, d = 1)
Change points detected from the factor-driven common component.
cs <- fs$common.out
cs$est.cp
Visualise the statistics involved in the multiscale moving window-based procedure for detecting change points in the common component.
par(mar = rep(2, 4), mfrow = c(2, 2))
for(rr in 1:length(cs$G.seq)){
matplot(cs$est.cp.list[[rr]]$norm.stat, type = 'l', xlab = 'time', ylab = '', main = paste('G = ', cs$est.cp.list[[rr]]$G, sep = '')) # change point detector from each frequency
lines(cs$est.cp.list[[rr]]$stat, col = 4, lwd = 2) # aggregation
abline(v = out$cp.common, col = 2, lty = 3) # true change points
abline(v = cs$est.cp.list[[rr]]$cp, col = 4, lty = 3) # change point estimators
abline(h = cs$est.cp.list[[rr]]$thr, col = 3) # threshold
}
Change points detected from the idiosyncratic VAR process.
is <- fs$idio.out
is$est.cp
Visualise the statistics involved in the multiscale scanning procedure for detecting change points in the idiosyncratic component.
par(mar = rep(2, 4), mfrow = c(2, 2))
for(rr in 1:length(is$G.seq)){
plot(is$est.cp.list[[rr]]$stat, type = 'l', xlab = 'time', ylab = '', main = paste('G = ', is$est.cp.list[[rr]]$G, sep = '')) # change point detector
abline(v = out$cp.idio, col = 2, lty = 3) # true change points
abline(v = is$est.cp.list[[rr]]$cp, col = 4, lty = 2) # change point estimators
abline(h = is$est.cp.list[[rr]]$thr, col = 4) # threshold
}
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