ets


def ets(
    target_col:str, # Name of the target variable column.
    trend:Optional[str]=None, # Trend component type.
    damped_trend:bool=False, # Whether to damp the trend.  Only meaningful when ``trend`` is not `None`.
    seasonal:Optional[str]=None, # Seasonal component type.
    seasonal_periods:Optional[int]=None, # Number of periods in a complete seasonal cycle — e.g. 12 for monthly data with an annual cycle.  Required when ``seasonal`` is not ``None``.
    initialization_method:Optional[str]='estimated', # How to initialise the recursions.  When `"known"` is chosen, `initial_level` (and `initial_trend` / `initial_seasonal` where applicable) must also be provided.
    initial_level:Optional[float]=None, # Initial level value.  Required when `initialization_method=`"known"`.
    initial_trend:Optional[float]=None, # Initial trend value.  Required when `initialization_method=`"known"` and the model has a trend component.
    initial_seasonal:Optional[list]=None, # Initial seasonal factors (length `seasonal_periods` or ``seasonal_periods - 1``).  Required when ``initialization_method="known"`` and the model is seasonal.
    bounds:Optional[dict]=None, # Parameter bounds passed to ``ExponentialSmoothing``, e.g. `{"smoothing_level": (0, 1)}`.
    dates:NoneType=None, # Datetime index for the series.  Inferred automatically when `endog` is a Pandas object with a `DatetimeIndex`.
    freq:Optional[str]=None, # Frequency of the time series (e.g. ``"M"``, ``"D"``).  Optional when `dates` is provided.
    missing:str='none', # How to handle ``NaN`` values in the input series.
    optimized:bool=True, # Estimate smoothing parameters by maximising the log-likelihood.
    smoothing_level:Optional[float]=None, # Fixed alpha value.  When set, this value is used directly and not optimised.
    smoothing_trend:Optional[float]=None, # Fixed beta value.  Only used when the model has a trend component.
    smoothing_seasonal:Optional[float]=None, # Fixed gamma value.  Only used when the model is seasonal.
    damping_trend:Optional[float]=None, # Fixed phi (damping) value.  Only used when ``damped_trend=True``.
    remove_bias:bool=False, # Remove bias from forecast values by enforcing that the mean residual is zero.
    start_params:NoneType=None, # Starting parameter values for the optimiser.
    method:Optional[str]=None, # Optimisation method — one of `"L-BFGS-B"` (default), `"TNC"`, `"SLSQP"`, `"Powell"`, `"trust-constr"`, `"basinhopping"` (alias `"bh"`), or `"least_squares"` (alias `"ls"`).
    minimize_kwargs:Optional[dict]=None, # Extra keyword arguments forwarded to the chosen SciPy minimiser.
    use_brute:bool=True, # Search for good starting values with a brute-force grid search before running the main optimiser.
    box_cox:Union[bool, float]=False, # Whether to apply Box-Cox transformation to the target variable. If a float value is provided, it will be used as the lambda parameter for the Box-Cox transformation. If True, the lambda parameter will be estimated from the data.
    box_cox_biasadj:bool=False, # Bias adjustment when inverting the manual Box-Cox on forecasts.
    fit_kwargs:Optional[dict]=None, # Any additional keyword arguments forwarded verbatim to `ExponentialSmoothing.fit`.
)->None: # Fitted model object with a ``forecast`` method for making predictions and properties for information criteria scores (AIC, BIC, etc.)

Holt-Winters Exponential Smoothing forecaster.

A thin wrapper around statsmodels.tsa.holtwinters.ExponentialSmoothing.

	Type	Default	Details
target_col	str		Name of the target variable column.
trend	Optional[str]	None	Trend component type.
damped_trend	bool	False	Whether to damp the trend. Only meaningful when `trend` is not `None`.
seasonal	Optional[str]	None	Seasonal component type.
seasonal_periods	Optional[int]	None	Number of periods in a complete seasonal cycle — e.g. 12 for monthly data with an annual cycle. Required when `seasonal` is not `None`.
initialization_method	Optional[str]	estimated	How to initialise the recursions. When `"known"` is chosen, `initial_level` (and `initial_trend` / `initial_seasonal` where applicable) must also be provided.
initial_level	Optional[float]	None	Initial level value. Required when `initialization_method=`“known”`. \| \| initial_trend \| Optional[float] \| None \| Initial trend value. Required when`initialization_method=`"known"` and the model has a trend component.
initial_seasonal	Optional[list]	None	Initial seasonal factors (length `seasonal_periods` or `seasonal_periods - 1`). Required when `initialization_method="known"` and the model is seasonal.
bounds	Optional[dict]	None	Parameter bounds passed to `ExponentialSmoothing`, e.g. `{"smoothing_level": (0, 1)}`.
dates	NoneType	None	Datetime index for the series. Inferred automatically when `endog` is a Pandas object with a `DatetimeIndex`.
freq	Optional[str]	None	Frequency of the time series (e.g. `"M"`, `"D"`). Optional when `dates` is provided.
missing	str	none	How to handle `NaN` values in the input series.
optimized	bool	True	Estimate smoothing parameters by maximising the log-likelihood.
smoothing_level	Optional[float]	None	Fixed alpha value. When set, this value is used directly and not optimised.
smoothing_trend	Optional[float]	None	Fixed beta value. Only used when the model has a trend component.
smoothing_seasonal	Optional[float]	None	Fixed gamma value. Only used when the model is seasonal.
damping_trend	Optional[float]	None	Fixed phi (damping) value. Only used when `damped_trend=True`.
remove_bias	bool	False	Remove bias from forecast values by enforcing that the mean residual is zero.
start_params	NoneType	None	Starting parameter values for the optimiser.
method	Optional[str]	None	Optimisation method — one of `"L-BFGS-B"` (default), `"TNC"`, `"SLSQP"`, `"Powell"`, `"trust-constr"`, `"basinhopping"` (alias `"bh"`), or `"least_squares"` (alias `"ls"`).
minimize_kwargs	Optional[dict]	None	Extra keyword arguments forwarded to the chosen SciPy minimiser.
use_brute	bool	True	Search for good starting values with a brute-force grid search before running the main optimiser.
box_cox	Union[bool, float]	False	Whether to apply Box-Cox transformation to the target variable. If a float value is provided, it will be used as the lambda parameter for the Box-Cox transformation. If True, the lambda parameter will be estimated from the data.
box_cox_biasadj	bool	False	Bias adjustment when inverting the manual Box-Cox on forecasts.
fit_kwargs	Optional[dict]	None	Any additional keyword arguments forwarded verbatim to `ExponentialSmoothing.fit`.
Returns	None		Fitted model object with a `forecast` method for making predictions and properties for information criteria scores (AIC, BIC, etc.)

source

ets.fit


def fit(
    df:pd.DataFrame, # Training DataFrame containing the target column
)->None:

Fit ExponentialSmoothing to the training data.

	Type	Details
df	pd.DataFrame	Training DataFrame containing the target column
Returns	None

source

ets.forecast


def forecast(
    H:int, # Forecast horizon.
    exog:Optional[pd.DataFrame]=None, # Accepted for API consistency with other models but silently ignored — ETS forecasts do not use exogenous variables.
)->np.ndarray: # Forecast values of length `H`.

Multi-step forecast.

	Type	Default	Details
H	int		Forecast horizon.
exog	Optional[pd.DataFrame]	None	Accepted for API consistency with other models but silently ignored — ETS forecasts do not use exogenous variables.
Returns	np.ndarray		Forecast values of length `H`.

source

ets.cross_validate


def cross_validate(
    df:pd.DataFrame, # Full dataset
    cv_split:int, # Number of CV folds.
    test_size:int, # Test window size per fold.
    metrics:List[Callable], # Metric functions (e.g. ``[MAE, RMSE]``) used to evaluate forecast accuracy across folds. Call ``.cv_summary()`` after cross-validation to retrieve the aggregated scores.
    step_size:int=1, # Step size to advance the test window each fold.
    h_split_point:Optional[int]=None, # Split the test window into two sub-horizons for separate short- and long-term evaluation.
)->Tuple[pd.DataFrame, pd.DataFrame]: # Summary DataFrame with mean metric scores across folds, and (optionally) a fold-level DataFrame with true vs. predicted values for each fold.

Run time-series cross-validation.

	Type	Default	Details
df	pd.DataFrame		Full dataset
cv_split	int		Number of CV folds.
test_size	int		Test window size per fold.
metrics	List[Callable]		Metric functions (e.g. `[MAE, RMSE]`) used to evaluate forecast accuracy across folds. Call `.cv_summary()` after cross-validation to retrieve the aggregated scores.
step_size	int	1	Step size to advance the test window each fold.
h_split_point	Optional[int]	None	Split the test window into two sub-horizons for separate short- and long-term evaluation.
Returns	Tuple[pd.DataFrame, pd.DataFrame]		Summary DataFrame with mean metric scores across folds, and (optionally) a fold-level DataFrame with true vs. predicted values for each fold.