smooth.OM

class smooth.OM(model='ZXZ', lags=None, ar_order=0, i_order=0, ma_order=0, orders=None, constant=False, formula=None, regressors='use', occurrence='odds-ratio', loss='likelihood', h=0, holdout=False, persistence=None, phi=None, initial='backcasting', n_iterations=None, arma=None, ic='AICc', bounds='usual', verbose=0, nlopt_kargs=None, ets='conventional', **kwargs)

Occurrence model — Python port of R’s om().

Inherits the ADAM API surface and overrides the bits that differ: cost function (Bernoulli likelihood with link transform), distribution (always "plogis"), scale (nan), and model-name format ("oETS(...)[F|O|I|D]").

Methods

fit(y[, X])	Fit the ADAM model to time series data.
outlierdummy([level, type])	Detect outliers and return a matrix of indicator dummy variables.
plot([which, level, legend, lowess])	Diagnostic plots for the fitted ADAM model (R: plot.adam).
predict(h[, X, interval, level, side, ...])	Probability forecast for the occurrence model.
predict_intervals(h[, X, levels, side, nsim])	Generate prediction intervals using the fitted ADAM model.
rmultistep([h])	Return the (T-h) × h matrix of rolling in-sample multistep forecast errors.
rstandard()	Return standardised residuals.
rstudent()	Return studentised (leave-one-out) residuals.
select_best_model()	Select the best model based on information criteria and update model parameters.
summary([digits])	Generate a formatted summary of the fitted model.

Attributes

actuals	Binary 0/1 occurrence indicator (matches R's actuals.om).
aic	Return Akaike Information Criterion.
aicc	Return corrected Akaike Information Criterion.
b_value	$B).
bic	Return Bayesian Information Criterion.
bicc	Return corrected Bayesian Information Criterion.
coef	Return estimated coefficients (parameter vector B).
constant_value	$constant).
data	$data).
distribution_	Always "plogis" for occurrence models.
error_type	'A' (additive) or 'M' (multiplicative).
fitted	In-sample fitted probabilities in [0, 1].
holdout_data	$holdout).
ic_weights	$ICw).
initial_type	$initialType).
initial_value	$initial).
is_combined	Return True if model is a combination of multiple models.
lags_used	Return the vector of lags used in the model.
loglik	Return log-likelihood of the fitted model.
loss_	$loss).
loss_value	$lossValue).
measurement	$measurement).
model_name	Model name in oETS(...)[X] form (uppercase X = F/O/I/D).
model_type	Return ETS model type code (e.g., 'AAN', 'AAA', 'MAdM').
models	$models).
n_param	$nParam).
nobs	Return number of observations used for fitting.
nparam	Return number of estimated parameters.
occurrence	Occurrence type used for fitting (e.g. "odds-ratio").
occurrence_char	Single-letter occurrence flag handed to the C++ fitter.
om_model	Fitted occurrence model (OM / OMG / AutoOM), or None.
orders	Return ARIMA orders as dict with 'ar', 'i', 'ma' keys.
persistence_vector	$persistence).
phi_	$phi).
profile	$profile).
residuals	ot - p_fitted (binary indicator minus fitted probability).
scale	Always NaN for occurrence models (no continuous error scale).
sigma	Same as scale.
states	$states).
time_elapsed	Time taken to fit the model in seconds.
transition	$transition).