Detect Structural Breaks
Detect structural breaks using the estimated structural time series model
stsm_detect_breaks(
model,
y,
components = c("trend", "cycle", "seasonal"),
freq = NULL,
exo = NULL,
sig_level = 0.01,
ci = 0.8,
smooth = TRUE,
plot = FALSE,
cores = NULL,
show_progress = FALSE
)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 |
Matrix of exogenous variables used for the historical data. Can be used to specify regression effects or other seasonal effects like holidays, etc. |
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 |
data table (or list of data tables) containing the dates of detected anomalies from the filtered and/or smoothed series
## 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)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.