autostsm (version 3.1.4)

stsm_detect_anomalies: Detect Anomalies

Description

Detect anomalies using the estimated structural time series model

Usage

stsm_detect_anomalies(
  model,
  y = NULL,
  freq = NULL,
  exo_obs = NULL,
  exo_state = NULL,
  sig_level = 0.01,
  smooth = TRUE,
  plot = FALSE
)

Value

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

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.

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

smooth

Whether or not to use the Kalman smoother

plot

Whether to plot everything

Examples

if (FALSE) {
#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)
}