naive


def naive(
    target_col:str, # Name of the target variable column.
    season_period:Optional[int]=None, # Seasonal period ``m``.  ``None`` selects the non-seasonal naïve method.  When provided and the training series is shorter than ``m``, ``forecast`` returns an array of ``NaN``.
    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.
)->None:

Naïve forecaster.

Two modes controlled by season_period:

Non-seasonal (season_period=None): every forecast step repeats the last observed value in the training series.
Seasonal (season_period=m): forecast values are taken from the last complete season and cycled forward — i.e. step h is predicted by y[T - m + ((h-1) % m)], where T is the last training index.

	Type	Default	Details
target_col	str		Name of the target variable column.
season_period	Optional[int]	None	Seasonal period `m`. `None` selects the non-seasonal naïve method. When provided and the training series is shorter than `m`, `forecast` returns an array of `NaN`.
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.
Returns	None

source

naive.fit


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

Store the values needed for naïve forecasting.

No statistical model is estimated. fit simply applies data_prep and records the training series so that forecast can replicate the correct naïve pattern.

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

source

naive.forecast


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

Generate naïve forecasts.

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

source

naive.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.