------------------------------------------------------------------------ source ### var ``` python def var( target_cols:List[str], # List of target column names to model. lags:Dict[str, Union[int, List[int]]], # Dictionary specifying lags for each target variable. Values can be an int (number of lags) or a list of specific lag indices. lag_transform:Optional[Dict[str, list]]=None, # Dictionary specifying lag-transform functions for each target variable. Each value is a list of transformation functions (e.g., rolling_mean, expanding_std) to apply to the lagged features of that target. difference:Optional[Dict[str, int]]=None, # Dictionary specifying the order of ordinary differencing to apply to each target variable. Values are integers indicating how many times to difference the series. seasonal_diff:Optional[Dict[str, int]]=None, # Dictionary specifying the seasonal period for seasonal differencing for each target variable. Values are integers indicating the seasonal lag (e.g., 12 for monthly data with yearly seasonality). trend:Optional[Dict[str, str]]=None, # Dictionary specifying the trend strategy for each target variable. Values can be 'linear' for linear trend removal or 'ets' for ETS-based trend removal. pol_degree:Optional[Union[int, Dict[str, int]]]=1, # Polynomial degree for linear trend removal. Can be a single integer applied to all targets or a dictionary specifying the degree for each target. ets_params:Optional[Dict[str, Any]]=None, # Dictionary specifying ETS model and fit parameters for each target variable when using 'ets' trend removal. Each value is a dictionary of parameters for the ExponentialSmoothing model and fitting process. change_points:Optional[Dict[str, List[int]]]=None, # Dictionary specifying change points for piecewise linear trend removal for each target variable. Values are lists of integer indices indicating where the trend should change. Only applicable when trend strategy is 'linear'. box_cox:Optional[Dict[str, Union[bool, float, int]]]=None, # Dictionary specifying whether to apply Box-Cox transformation to each target variable. Values can be a boolean (True to apply, False to skip) or a float (lambda parameter for Box-Cox transformation). If True, lambda will be estimated from the data. box_cox_biasadj:Union[bool, Dict[str, bool]]=False, # Whether to apply bias adjustment when inverting the Box-Cox transformation on forecasts. Can be a single boolean applied to all targets or a dictionary specifying the bias adjustment for each target. add_constant:bool=True, # If True, a constant column will be added to the regressor matrix for the VAR model. This is typically used to allow for an intercept in the model. cat_variables:Optional[List[str]]=None, # List of categorical feature column names to encode. These will be shared across all target variables. categorical_encoder:Optional[Union[Dict[str, Any], Any]]=None, # A categorical encoder instance, or a single-entry dictionary mapping the target column to the encoder when the encoder requires access to the target variable during fitting (e.g. {target_col: MeanEncoder()}). If encoder requiring target access is provided directly without the dict format, first target column in target_cols will be used for fitting the encoder. For encoders that do not require target access, pass the encoder instance directly (e.g. OneHotEncoder()). verbose:bool=False, # If True, the model will print verbose messages. )->None: ``` *“* Initialize the VAR model with specified preprocessing and modeling parameters.

	Type	Default	Details
target_cols	List[str]		List of target column names to model.
lags	Dict[str, Union[int, List[int]]]		Dictionary specifying lags for each target variable. Values can be an int (number of lags) or a list of specific lag indices.
lag_transform	Optional[Dict[str, list]]	None	Dictionary specifying lag-transform functions for each target variable. Each value is a list of transformation functions (e.g., rolling_mean, expanding_std) to apply to the lagged features of that target.
difference	Optional[Dict[str, int]]	None	Dictionary specifying the order of ordinary differencing to apply to each target variable. Values are integers indicating how many times to difference the series.
seasonal_diff	Optional[Dict[str, int]]	None	Dictionary specifying the seasonal period for seasonal differencing for each target variable. Values are integers indicating the seasonal lag (e.g., 12 for monthly data with yearly seasonality).
trend	Optional[Dict[str, str]]	None	Dictionary specifying the trend strategy for each target variable. Values can be ‘linear’ for linear trend removal or ‘ets’ for ETS-based trend removal.
pol_degree	Optional[Union[int, Dict[str, int]]]	1	Polynomial degree for linear trend removal. Can be a single integer applied to all targets or a dictionary specifying the degree for each target.
ets_params	Optional[Dict[str, Any]]	None	Dictionary specifying ETS model and fit parameters for each target variable when using ‘ets’ trend removal. Each value is a dictionary of parameters for the ExponentialSmoothing model and fitting process.
change_points	Optional[Dict[str, List[int]]]	None	Dictionary specifying change points for piecewise linear trend removal for each target variable. Values are lists of integer indices indicating where the trend should change. Only applicable when trend strategy is ‘linear’.
box_cox	Optional[Dict[str, Union[bool, float, int]]]	None	Dictionary specifying whether to apply Box-Cox transformation to each target variable. Values can be a boolean (True to apply, False to skip) or a float (lambda parameter for Box-Cox transformation). If True, lambda will be estimated from the data.
box_cox_biasadj	Union[bool, Dict[str, bool]]	False	Whether to apply bias adjustment when inverting the Box-Cox transformation on forecasts. Can be a single boolean applied to all targets or a dictionary specifying the bias adjustment for each target.
add_constant	bool	True	If True, a constant column will be added to the regressor matrix for the VAR model. This is typically used to allow for an intercept in the model.
cat_variables	Optional[List[str]]	None	List of categorical feature column names to encode. These will be shared across all target variables.
categorical_encoder	Optional[Union[Dict[str, Any], Any]]	None	A categorical encoder instance, or a single-entry dictionary mapping the target column to the encoder when the encoder requires access to the target variable during fitting (e.g. {target_col: MeanEncoder()}). If encoder requiring target access is provided directly without the dict format, first target column in target_cols will be used for fitting the encoder. For encoders that do not require target access, pass the encoder instance directly (e.g. OneHotEncoder()).
verbose	bool	False	If True, the model will print verbose messages.
Returns	None

------------------------------------------------------------------------ source ### var.fit ``` python def fit( df:pd.DataFrame, # Training DataFrame containing the target and any feature columns. )->None: ``` *Fit the VAR model to the provided DataFrame.*

	Type	Details
df	pd.DataFrame	Training DataFrame containing the target and any feature columns.
Returns	None

------------------------------------------------------------------------ source ### var.forecast ``` python def forecast( H:int, # Forecast horizon (number of steps ahead to predict). exog:Optional[pd.DataFrame]=None, # Future exogenous regressors (must contain at least H rows). )->Dict[str, np.ndarray]: # Forecasted values for each target, keyed by column name. ``` *Generate forecasts for H future time steps.*

	Type	Default	Details
H	int		Forecast horizon (number of steps ahead to predict).
exog	Optional[pd.DataFrame]	None	Future exogenous regressors (must contain at least H rows).
Returns	Dict[str, np.ndarray]		Forecasted values for each target, keyed by column name.

------------------------------------------------------------------------ source ### var.cross_validate ``` python def cross_validate( df:pd.DataFrame, # Input dataframe. target_col:str, # Target variable for evaluation. cv_split:int, # Number of cross-validation folds. test_size:int, # Test 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 for rolling window. Default is 1. h_split_point:Optional[int]=None, # Point to split the test set for separate evaluation. Default is None. )->Union[pd.DataFrame, Tuple[pd.DataFrame, pd.DataFrame]]: # DataFrame with averaged cross-validation metric scores. ``` *Perform cross-validation.*

	Type	Default	Details
df	pd.DataFrame		Input dataframe.
target_col	str		Target variable for evaluation.
cv_split	int		Number of cross-validation folds.
test_size	int		Test 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 for rolling window. Default is 1.
h_split_point	Optional[int]	None	Point to split the test set for separate evaluation. Default is None.
Returns	Union[pd.DataFrame, Tuple[pd.DataFrame, pd.DataFrame]]		DataFrame with averaged cross-validation metric scores.