stsm_detect_breaks {autostsm}

Detect Structural Breaks

Description

Detect structural breaks using the estimated structural time series model

Usage

stsm_detect_breaks(
  model,
  y,
  components = c("trend", "cycle", "seasonal"),
  freq = NULL,
  exo_obs = NULL,
  exo_state = NULL,
  sig_level = 0.01,
  ci = 0.8,
  smooth = TRUE,
  plot = FALSE,
  cores = NULL,
  show_progress = FALSE
)

Arguments

model

Structural time series model estimated using stsm_estimate.

y

Univariate time series of data values. May also be a 2 column data frame containing a date column.

components

Vector of components to test for structural breaks

freq

Frequency of the data (1 (yearly), 4 (quarterly), 12 (monthly), 365.25/7 (weekly), 365.25 (daily)), default is NULL and will be automatically detected

exo_obs

Matrix of exogenous variables to be used in the observation equation.

exo_state

Matrix of exogenous variables to be used in the state matrix.

sig_level

Significance level to determine statistically significant anomalies

ci

Confidence interval, value between 0 and 1 exclusive.

smooth

Whether or not to use the Kalman smoother

plot

Whether to plot everything

cores

Number of cores to use for break detection

show_progress

Whether to show progress bar

Value

data table (or list of data tables) containing the dates of detected anomalies from the filtered and/or smoothed series

Examples

## Not run: 
#GDP Not seasonally adjusted
library(autostsm)
data("NA000334Q", package = "autostsm") #From FRED
NA000334Q = data.table(NA000334Q, keep.rownames = TRUE)
colnames(NA000334Q) = c("date", "y")
NA000334Q[, "date" := as.Date(date)]
NA000334Q[, "y" := as.numeric(y)]
NA000334Q = NA000334Q[date >= "1990-01-01", ]
stsm = stsm_estimate(NA000334Q)
breaks = stsm_detect_breaks(model = stsm, y = NA000334Q, plot = TRUE, cores = 2)

## End(Not run)