exceptions

skforecast.exceptions.exceptions.DataTypeWarning

DataTypeWarning(message)

Bases: UserWarning

Warning used to notify there are dtypes in the exogenous data that are not 'int', 'float', 'bool' or 'category'. Most machine learning models do not accept other data types, therefore the forecaster fit and predict may fail.

Source code in skforecast\exceptions\exceptions.py

def __init__(self, message):
    self.message = message

skforecast.exceptions.exceptions.DataTransformationWarning

DataTransformationWarning(message)

Bases: UserWarning

Warning used to notify that the output data is in the transformed space.

Source code in skforecast\exceptions\exceptions.py

def __init__(self, message):
    self.message = message

skforecast.exceptions.exceptions.IgnoredArgumentWarning

IgnoredArgumentWarning(message)

Bases: UserWarning

Warning used to notify that an argument is ignored when using a method or a function.

Source code in skforecast\exceptions\exceptions.py

def __init__(self, message):
    self.message = message

skforecast.exceptions.exceptions.LongTrainingWarning

LongTrainingWarning(message)

Bases: UserWarning

Warning used to notify that a large number of models will be trained and the the process may take a while to run.

Source code in skforecast\exceptions\exceptions.py

def __init__(self, message):
    self.message = message

skforecast.exceptions.exceptions.MissingExogWarning

MissingExogWarning(message)

Bases: UserWarning

Warning used to indicate that there are missing exogenous variables in the data. Most machine learning models do not accept missing values, so the Forecaster's fit' andpredict' methods may fail.

Source code in skforecast\exceptions\exceptions.py

def __init__(self, message):
    self.message = message

skforecast.exceptions.exceptions.MissingValuesWarning

MissingValuesWarning(message)

Bases: UserWarning

Warning used to indicate that there are missing values in the data. This warning occurs when the input data contains missing values, or the training matrix generates missing values. Most machine learning models do not accept missing values, so the Forecaster's fit' andpredict' methods may fail.

Source code in skforecast\exceptions\exceptions.py

def __init__(self, message):
    self.message = message

skforecast.exceptions.exceptions.OneStepAheadValidationWarning

OneStepAheadValidationWarning(message)

Bases: UserWarning

Warning used to notify that the one-step-ahead validation is being used.

Source code in skforecast\exceptions\exceptions.py

def __init__(self, message):
    self.message = message

skforecast.exceptions.exceptions.UnknownLevelWarning

UnknownLevelWarning(message)

Bases: UserWarning

Warning used to notify that a level being predicted was not part of the training data.

Source code in skforecast\exceptions\exceptions.py

def __init__(self, message):
    self.message = message

skforecast.exceptions.exceptions.SaveLoadSkforecastWarning

SaveLoadSkforecastWarning(message)

Bases: UserWarning

Warning used to notify any issues that may arise when saving or loading a forecaster.

Source code in skforecast\exceptions\exceptions.py

def __init__(self, message):
    self.message = message

skforecast.exceptions.exceptions.SkforecastVersionWarning

SkforecastVersionWarning(message)

Bases: UserWarning

Warning used to notify that the skforecast version installed in the environment differs from the version used to initialize the forecaster.

Source code in skforecast\exceptions\exceptions.py

def __init__(self, message):
    self.message = message