smooth.AutoADAM

class smooth.AutoADAM(model='ZXZ', lags=None, ar_order=None, i_order=None, ma_order=None, orders=None, arima_select=True, distribution=None, outliers='ignore', level=0.99, constant=False, ic='AICc', loss='likelihood', h=None, holdout=False, bounds='usual', initial='backcasting', regressors='use', verbose=0, **kwargs)

Automatic ADAM model selection.

Wraps ADAM with automatic selection of ARIMA orders and error distribution, mirroring R’s auto.adam() function.

ETS model selection (ZZZ, ZXZ, FFF, CCC …) is handled by the underlying ADAM machinery and is not duplicated here.

Parameters:

model (Union[str, List[str]], default="ZXZ") – ETS model specification passed to each internal ADAM fit. Supports all codes accepted by ADAM.
lags (Optional[List[int]], default=None) – Seasonal period(s). Lag 1 is prepended automatically when absent.
ar_order (Union[int, List[int]], default=[3, 3]) – Maximum AR order(s) per lag level for ARIMA selection. Defaults to [3, 3] matching R’s auto.adam().
i_order (Union[int, List[int]], default=[2, 1]) – Maximum integration order(s) per lag level for ARIMA selection. Defaults to [2, 1] matching R’s auto.adam().
ma_order (Union[int, List[int]], default=[3, 3]) – Maximum MA order(s) per lag level for ARIMA selection. Defaults to [3, 3] matching R’s auto.adam().
orders (Optional[Dict[str, Any]], default=None) – R-style alternative to scalar max orders. A dict with keys "ar", "i", "ma" (each an int or list) and optionally "select" (bool). When provided, ar_order/i_order/ ma_order are ignored.
arima_select (bool, default=True) – Whether to perform ARIMA order selection. Unlike ADAM, this defaults to True.
distribution (Union[str, List[str]], default=(all 7 distributions)) – Distribution(s) to try. When a list is supplied every entry is fitted and the one with lowest IC is kept. A single string uses only that distribution (no selection loop).
outliers (Literal["ignore", "use", "select"], default="ignore") – Outlier handling mode. "use" and "select" are accepted but not yet implemented; a UserWarning is issued.
level (float, default=0.99) – Confidence level for outlier detection (placeholder for future use).
ic (Literal["AIC", "AICc", "BIC", "BICc"], default="AICc") – Information criterion used for all model comparisons.
loss (LOSS_OPTIONS, default="likelihood") – Loss function for parameter estimation.
constant (Union[bool, float], default=False) – Constant/drift term. Overridden by ARIMA selection when arima_select=True (the selection algorithm tests constant on/off).
holdout (bool, default=False) – Whether to use a holdout sample.
h (Optional[int], default=None) – Forecast horizon.
bounds (Literal["usual", "admissible", "none"], default="usual") – Parameter bounds type.
initial (str or dict, default="backcasting") – Initialisation method or fixed initial values.
regressors (Literal["use", "select", "adapt"], default="use") – How to handle external regressors.
verbose (int, default=0) – Verbosity level for the final model fit. All intermediate selection fits are always silent.
**kwargs – Additional arguments forwarded to every internal ADAM fit.

See also

ADAM: Full ADAM model.
ES: ETS-only wrapper.
MSARIMA: Pure ARIMA wrapper.

Examples

Full automatic selection (ETS + ARIMA + distribution):

>>> from smooth import AutoADAM
>>> import numpy as np
>>> y = np.cumsum(np.random.randn(120)) + 100
>>> model = AutoADAM(lags=[12])
>>> model.fit(y)
>>> print(model.model, model.distribution_)

ARIMA-only automatic selection (no ETS):

>>> model = AutoADAM(model="NNN", lags=[1, 12])
>>> model.fit(y)

Fix the distribution, only select ARIMA orders:

>>> model = AutoADAM(distribution="dnorm", lags=[1, 12])
>>> model.fit(y)

References

Svetunkov, I. (2023). Forecasting and Analytics with the Augmented Dynamic Adaptive Model. https://openforecast.org/adam/

Methods

`fit`(y[, X])	Fit the AutoADAM model.
`outlierdummy`([level, type])	Detect outliers and return a matrix of indicator dummy variables.
`predict`(h[, X, interval, level, side, ...])	Generate forecasts using the fitted ADAM model.
`predict_intervals`(h[, X, levels, side, nsim])	Generate prediction intervals using the fitted ADAM model.
`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`	Return original in-sample data.
`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_`	$distribution).
`error_type`	'A' (additive) or 'M' (multiplicative).
`fitted`	Return in-sample fitted values.
`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`	modelName()).
`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.
`orders`	Return ARIMA orders as dict with 'ar', 'i', 'ma' keys.
`persistence_vector`	$persistence).
`phi_`	$phi).
`profile`	$profile).
`residuals`	Return model residuals (errors from fitting).
`scale`	$scale).
`sigma`	Return scale/standard error estimate.
`states`	$states).
`time_elapsed`	Time taken to fit the model in seconds.
`transition`	$transition).