------------------------------------------------------------------------ source ### naive ``` python 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 ``` python 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 ``` python 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 ``` python 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.