ForecasterBaseline

ForecasterEquivalentDate

ForecasterEquivalentDate(offset, n_offsets=1, agg_func=np.mean, forecaster_id=None)

This forecaster predicts future values based on the most recent equivalent date. It also allows to aggregate multiple past values of the equivalent date using a function (e.g. mean, median, max, min, etc.). The equivalent date is calculated by moving back in time a specified number of steps (offset). The offset can be defined as an integer or as a pandas DateOffset. This approach is useful as a baseline, but it is a simplistic method and may not capture complex underlying patterns.

Parameters:

Name	Type	Description	Default
offset	(int, DateOffset)	Number of steps to go back in time to find the most recent equivalent date to the target period. If offset is an integer, it represents the number of steps to go back in time. For example, if the frequency of the time series is daily, offset = 7 means that the most recent data similar to the target period is the value observed 7 days ago. Pandas DateOffsets can also be used to move forward a given number of valid dates. For example, Bday(2) can be used to move back two business days. If the date does not start on a valid date, it is first moved to a valid date. For example, if the date is a Saturday, it is moved to the previous Friday. Then, the offset is applied. If the result is a non-valid date, it is moved to the next valid date. For example, if the date is a Sunday, it is moved to the next Monday. For more information about offsets, see https://pandas.pydata.org/docs/reference/offset_frequency.html.	required
n_offsets	int	Number of equivalent dates (multiple of offset) used in the prediction. If n_offsets is greater than 1, the values at the equivalent dates are aggregated using the agg_func function. For example, if the frequency of the time series is daily, offset = 7, n_offsets = 2 and agg_func = np.mean, the predicted value will be the mean of the values observed 7 and 14 days ago.	`1`
agg_func	Callable	Function used to aggregate the values of the equivalent dates when the number of equivalent dates (n_offsets) is greater than 1.	`np.mean`
forecaster_id	(str, int)	Name used as an identifier of the forecaster.	`None`

Attributes:

Name	Type	Description
offset	(int, DateOffset)	Number of steps to go back in time to find the most recent equivalent date to the target period. If offset is an integer, it represents the number of steps to go back in time. For example, if the frequency of the time series is daily, offset = 7 means that the most recent data similar to the target period is the value observed 7 days ago. Pandas DateOffsets can also be used to move forward a given number of valid dates. For example, Bday(2) can be used to move back two business days. If the date does not start on a valid date, it is first moved to a valid date. For example, if the date is a Saturday, it is moved to the previous Friday. Then, the offset is applied. If the result is a non-valid date, it is moved to the next valid date. For example, if the date is a Sunday, it is moved to the next Monday. For more information about offsets, see https://pandas.pydata.org/docs/reference/offset_frequency.html.
n_offsets	int	Number of equivalent dates (multiple of offset) used in the prediction. If offset is greater than 1, the value at the equivalent dates is aggregated using the agg_func function. For example, if the frequency of the time series is daily, offset = 7, n_offsets = 2 and agg_func = np.mean, the predicted value will be the mean of the values observed 7 and 14 days ago.
agg_func	Callable	Function used to aggregate the values of the equivalent dates when the number of equivalent dates (n_offsets) is greater than 1.
window_size	int	Number of past values needed to include the last equivalent dates according to the offset and n_offsets.
last_window	pandas Series	This window represents the most recent data observed by the predictor during its training phase. It contains the past values needed to include the last equivalent date according the offset and n_offsets.
index_type	type	Type of index of the input used in training.
index_freq	str	Frequency of Index of the input used in training.
training_range	pandas Index	First and last values of index of the data used during training.
fitted	bool	Tag to identify if the regressor has been fitted (trained).
creation_date	str	Date of creation.
fit_date	str	Date of last fit.
skforecast_version	str	Version of skforecast library used to create the forecaster.
python_version	str	Version of python used to create the forecaster.
forecaster_id	(str, int)	Name used as an identifier of the forecaster.
regressor	Ignored	Not used, present here for API consistency by convention.
differentiation	Ignored	Not used, present here for API consistency by convention.

Source code in skforecast\ForecasterBaseline\ForecasterEquivalentDate.py

def __init__(
    self,
    offset: Union[int, pd.tseries.offsets.DateOffset],
    n_offsets: int=1,
    agg_func: Callable=np.mean,
    forecaster_id: Optional[Union[str, int]]=None
) -> None:

    self.offset             = offset
    self.n_offsets          = n_offsets
    self.agg_func           = agg_func
    self.last_window        = None
    self.index_type         = None
    self.index_freq         = None
    self.training_range     = None
    self.fitted             = False
    self.creation_date      = pd.Timestamp.today().strftime('%Y-%m-%d %H:%M:%S')
    self.fit_date           = None
    self.skforecast_version = skforecast.__version__
    self.python_version     = sys.version.split(" ")[0]
    self.forecaster_id      = forecaster_id
    self.regressor          = None
    self.differentiation    = None

    if not isinstance(self.offset, (int, pd.tseries.offsets.DateOffset)):
        raise TypeError(
            ("`offset` must be an integer greater than 0 or a "
             "pandas.tseries.offsets. Find more information about offsets in "
             "https://pandas.pydata.org/docs/reference/offset_frequency.html")
        )

    self.window_size = self.offset * self.n_offsets

fit(y, exog=None, store_in_sample_residuals=None)

Training Forecaster.

Parameters:

Name	Type	Description	Default
y	pandas Series	Training time series.	required
exog	Ignored	Not used, present here for API consistency by convention.	None
store_in_sample_residuals	Ignored	Not used, present here for API consistency by convention.	None

Returns:

Type	Description
None

Source code in skforecast\ForecasterBaseline\ForecasterEquivalentDate.py

def fit(
    self,
    y: pd.Series,
    exog: Any=None,
    store_in_sample_residuals: Any=None
) -> None:
    """
    Training Forecaster.

    Parameters
    ----------
    y : pandas Series
        Training time series.
    exog : Ignored
        Not used, present here for API consistency by convention.
    store_in_sample_residuals : Ignored
        Not used, present here for API consistency by convention.

    Returns
    -------
    None

    """

    if isinstance(self.offset, pd.tseries.offsets.DateOffset):
        if not isinstance(y.index, pd.DatetimeIndex):
            raise TypeError(
                ("If `offset` is a pandas DateOffset, the index of `y` must be a "
                 "pandas DatetimeIndex with frequency.")
            )
        elif y.index.freq is None:
            raise TypeError(
                ("If `offset` is a pandas DateOffset, the index of `y` must be a "
                 "pandas DatetimeIndex with frequency.")
            )

    # Reset values in case the forecaster has already been fitted.
    self.index_type     = None
    self.index_freq     = None
    self.last_window    = None
    self.fitted         = False
    self.training_range = None

    _, y_index = preprocess_y(y=y, return_values=False)

    if isinstance(self.offset, pd.tseries.offsets.DateOffset):
        # Calculate the window_size in steps for compatibility with the
        # check_predict_input function. This is not a exact calculation
        # because the offset follows the calendar rules and the distance
        # between two dates may not be constant.
        first_valid_index = (y_index[-1] - self.offset * self.n_offsets)

        try:
            window_size_idx_start = y_index.get_loc(first_valid_index)
            window_size_idx_end = y_index.get_loc(y_index[-1])
            self.window_size = window_size_idx_end - window_size_idx_start
        except KeyError:
            raise ValueError(
                (f"The length of `y` ({len(y)}), must be greater than or equal "
                 f"to the window size ({self.window_size}). This is because  "
                 f"the offset ({self.offset}) is larger than the available "
                 f"data. Try to decrease the size of the offset ({self.offset}), "
                 f"the number of n_offsets ({self.n_offsets}) or increase the "
                 f"size of `y`.")
            )
    else:
        if len(y) < self.window_size:
            raise ValueError(
                (f"The length of `y` ({len(y)}), must be greater than or equal "
                 f"to the window size ({self.window_size}). This is because  "
                 f"the offset ({self.offset}) is larger than the available "
                 f"data. Try to decrease the size of the offset ({self.offset}), "
                 f"the number of n_offsets ({self.n_offsets}) or increase the "
                 f"size of `y`.")
            )

    self.fitted = True
    self.fit_date = pd.Timestamp.today().strftime('%Y-%m-%d %H:%M:%S')
    self.training_range = y_index[[0, -1]]
    self.index_type = type(y_index)
    self.index_freq = (
        y_index.freqstr if isinstance(y_index, pd.DatetimeIndex) else y_index.step
    )

    # The last time window of training data is stored so that equivalent
    # dates are available when calling the `predict` method.
    # Store the whole series to avoid errors when the offset is larger 
    # than the data available.
    self.last_window = y.copy()

predict(steps, last_window=None, exog=None)

Predict n steps ahead.

Parameters:

Name	Type	Description	Default
steps	int	Number of future steps predicted.	required
last_window	pandas Series	Past values needed to select the last equivalent dates according to the offset. If last_window = None, the values stored in self.last_window are used and the predictions start immediately after the training data.	`None`
exog	Ignored	Not used, present here for API consistency by convention.	None

Returns:

Name	Type	Description
predictions	pandas Series	Predicted values.

Source code in skforecast\ForecasterBaseline\ForecasterEquivalentDate.py

def predict(
    self,
    steps: int,
    last_window: Optional[pd.Series]=None,
    exog: Any=None,
) -> pd.Series:
    """
    Predict n steps ahead.

    Parameters
    ----------
    steps : int
        Number of future steps predicted.
    last_window : pandas Series, default `None`
        Past values needed to select the last equivalent dates according to 
        the offset. If `last_window = None`, the values stored in 
        `self.last_window` are used and the predictions start immediately 
        after the training data.
    exog : Ignored
        Not used, present here for API consistency by convention.

    Returns
    -------
    predictions : pandas Series
        Predicted values.

    """

    if last_window is None:
        last_window = self.last_window

    check_predict_input(
        forecaster_name  = type(self).__name__,
        steps            = steps,
        fitted           = self.fitted,
        included_exog    = None,
        index_type       = self.index_type,
        index_freq       = self.index_freq,
        window_size      = self.window_size,
        last_window      = last_window,
        last_window_exog = None,
        exog             = None,
        exog_type        = None,
        exog_col_names   = None,
        interval         = None,
        alpha            = None,
        max_steps        = None,
        levels           = None,
        series_col_names = None
    )

    last_window = last_window.copy()

    last_window_values, last_window_index = preprocess_last_window(
                                                last_window = last_window
                                            )

    if isinstance(self.offset, int):

        equivalent_indexes = np.tile(
                                 np.arange(-self.offset, 0),
                                 int(np.ceil(steps/self.offset))
                             )
        equivalent_indexes = equivalent_indexes[:steps]

        if self.n_offsets == 1:
            equivalent_values = last_window_values[equivalent_indexes]
            predictions = equivalent_values.ravel()

        if self.n_offsets > 1:
            equivalent_indexes = [equivalent_indexes - n * self.offset 
                                  for n in np.arange(self.n_offsets)]
            equivalent_indexes = np.vstack(equivalent_indexes)
            equivalent_values = last_window_values[equivalent_indexes]
            predictions = np.apply_along_axis(
                              self.agg_func,
                              axis = 0,
                              arr  = equivalent_values
                          )

        predictions = pd.Series(
                          data  = predictions,
                          index = expand_index(index=last_window_index, steps=steps),
                          name  = 'pred'
                      )

    if isinstance(self.offset, pd.tseries.offsets.DateOffset):

        predictions_index = expand_index(index=last_window_index, steps=steps)
        max_allowed_date = last_window_index[-1]

        # For every date in predictions_index, calculate the n offsets
        offset_dates = []
        for date in predictions_index:
            selected_offsets = []
            while len(selected_offsets) < self.n_offsets:
                offset_date = date - self.offset
                if offset_date <= max_allowed_date:
                    selected_offsets.append(offset_date)
                date = offset_date
            offset_dates.append(selected_offsets)

        offset_dates = np.array(offset_dates)

        # Select the values of the time series corresponding to the each
        # offset date. If the offset date is not in the time series, the
        # value is set to NaN.
        equivalent_values = (
            last_window.
            reindex(offset_dates.ravel())
            .to_numpy()
            .reshape(-1, self.n_offsets)
        )
        equivalent_values = pd.DataFrame(
                                data    = equivalent_values,
                                index   = predictions_index,
                                columns = [f'offset_{i}' 
                                           for i in range(self.n_offsets)]
                            )

        # Error if all values are missing
        if equivalent_values.isnull().all().all():
            raise ValueError(
                (f"All equivalent values are missing. This is caused by using "
                 f"an offset ({self.offset}) larger than the available data. "
                 f"Try to decrease the size of the offset ({self.offset}), "
                 f"the number of n_offsets ({self.n_offsets}) or increase the "
                 f"size of `last_window`. In backtesting, this error may be "
                 f"caused by using an `initial_train_size` too small.")
            )

        # Warning if equivalent values are missing
        incomplete_offsets = equivalent_values.isnull().any(axis=1)
        incomplete_offsets = incomplete_offsets[incomplete_offsets].index
        if not incomplete_offsets.empty:
            warnings.warn(
                (f"Steps: {incomplete_offsets.strftime('%Y-%m-%d').to_list()} "
                 f"are calculated with less than {self.n_offsets} n_offsets. "
                 f"To avoid this, increase the `last_window` size or decrease "
                 f"the number of n_offsets. The current configuration requires " 
                 f"a total offset of {self.offset * self.n_offsets}.")
            )

        aggregate_values = equivalent_values.apply(self.agg_func, axis=1)
        predictions = aggregate_values.rename('pred')

    return predictions