om: Occurrence Model

In smooth: Forecasting Using State Space Models

Occurrence Model

Description

Function returns the occurrence part of the ADAM model with the specified probability update and model types.

Usage

om(data, model = "ZXZ", lags = c(frequency(data)), orders = list(ar =
  c(0), i = c(0), ma = c(0), select = FALSE), constant = FALSE,
  formula = NULL, regressors = c("use", "select", "adapt"),
  occurrence = c("auto", "fixed", "odds-ratio", "inverse-odds-ratio",
  "direct", "general"), loss = c("likelihood", "MSE", "MAE", "HAM", "LASSO",
  "RIDGE"), h = 0, holdout = FALSE, persistence = NULL, phi = NULL,
  initial = c("backcasting", "optimal", "two-stage", "complete"),
  arma = NULL, ic = c("AICc", "AIC", "BIC", "BICc"), bounds = c("usual",
  "admissible", "none"), ets = c("conventional", "adam"), silent = TRUE,
  ...)

Arguments

data	Numeric vector, time series, or data frame. Non-binary input is automatically binarised: any non-zero value becomes 1.
model	Three-letter ETS specification such as "MNN" or "AAN". Automatic selection with "Z" / "X" / "Y" wildcards is supported.
lags	Vector of seasonal lags. Defaults to frequency(data).
orders	ARIMA orders list: list(ar, i, ma, select).
constant	Logical; whether to include a constant term.
formula	Optional formula for external regressors.
regressors	How to handle regressors: "use", "select", or "adapt".
occurrence	Type of link function mapping state to probability: "fixed" (constant), "odds-ratio", "inverse-odds-ratio", or "direct".
loss	Loss function: "likelihood" (Bernoulli) or "MSE".
h	Forecast horizon.
holdout	If TRUE, a holdout sample of size h is withheld.
persistence	Optional persistence (smoothing) parameter vector.
phi	Optional damping parameter.
initial	Initialisation method: "backcasting", "optimal", "two-stage", or "complete".
arma	Optional fixed ARMA parameters.
ic	Information criterion for model selection.
bounds	Parameter bounds type.
ets	Type of ETS model: "conventional" or "adam".
silent	If TRUE, suppresses output and plot.
...	Additional arguments passed to the optimiser (maxeval, xtol_rel, algorithm, print_level).

Details

The function estimates probability of demand occurrence, using the selected ADAM state space model. It supports ETS, ARIMA and explanatory variables, also allowing to have multiple frequencies and doing variables selection. It is an ADAM analogue for the binary occurrence variable modelling.

For the details about the model and its implementation, see the respective vignette: vignette("om","smooth")

Value

An object of class c("om","adam","smooth").

See Also

forecast.om, adam

Examples

set.seed(42)
y <- rbinom(120, 1, 0.6)
m <- om(y, model="MNN", occurrence="odds-ratio")
forecast(m, h=12)

smooth documentation built on June 21, 2026, 9:06 a.m.