Skip to contents

Segmented Smoothing around Breakpoints

Source: R/segmented_smoothing.R
segmented_smoothing.Rd

Estimates the trend-cycle component by segmenting a time series around known structural breakpoints.

Usage

segmented_smoothing(
  x,
  breaks = NULL,
  break_method = c("one-side", "two-sides"),
  smoothing_method = c("henderson_smoothing", "clf_smoothing"),
  ...
)

Arguments

x

An input time series (object of class "ts" or "mts").

breaks

A vector or list of breakpoints (dates) where the series should be split for smoothing.

break_method

Character string specifying the method used to handle boundaries at breakpoints: "one-side" or "two-sides" (the default). See details.

smoothing_method

Function specifying the smoothing method to apply to each segment. Must be henderson_smoothing() (the default) or clf_smoothing().

...

Additional arguments passed to smoothing_method.

Value

An object of class c("tc_estimates", "henderson") if smoothing_method is henderson_smoothing(), or c("tc_estimates", "clf") if smoothing_method is clf_smoothing().

Details

Two methods are available to estimate the trend-cycle around breakpoints:

  1. "one-side": The trend-cycle is estimated across the entire dataset, and the estimates after each breakpoint are overwritten with the estimates obtained by smoothing only the data following that breakpoint.

  2. "two-sides": The trend-cycle is estimated independently on each segment defined by the breakpoints, and the resulting estimates are combined to form the final trend-cycle.

The parameters field of the returned object corresponds to the parameters of the last segment (used to compute confidence intervals and implicit forecasts).

Examples

if (FALSE) { # rjd3jars::check_java_version(silent = TRUE)
x <- window(publishTC::french_ipi[, "manufacturing"], start = 2015)
tc_h <- henderson_smoothing(x, length = 13)$tc
breaks <- list(c(2020, 3))
tc_h_2s <- segmented_smoothing(x, breaks = breaks, break_method = "two-sides", length = 13)$tc
tc_h_rob <- henderson_robust_smoothing(x, ls = c(2020 + (3 - 1) / 12, 2020 + (4 - 1) / 12))$tc

plot(window(x, start = 2019.5, end = 2021),
  main = "Smoothing around COVID-19",
  xlab = NULL, ylab = NULL,
  lty = 3
)
lines(tc_h, col = "purple")
lines(tc_h_2s, col = "lightblue")
lines(tc_h_rob, col = "orange")
legend(
  "bottomright",
  legend = c("y", "Henderson", "Two-Sides Segmented", "Robust H. (2 LS)"),
  col = c("black", "purple", "lightblue", "orange"),
  lty = c(3, 1, 1, 1, 1),
  cex = 0.7
)
}