Detect Anomalies
Detect anomalies using the estimated structural time series model
stsm_detect_anomalies( model, y = NULL, freq = NULL, exo = NULL, sig_level = 0.01, smooth = TRUE, plot = 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. |
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 |
smooth |
Whether or not to use the Kalman smoother |
plot |
Whether to plot everything |
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)
anomalies = stsm_detect_anomalies(model = stsm, y = NA000334Q, plot = TRUE)
## End(Not run)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.