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preprocessing

TimeSeriesDifferentiator(order=1)

Bases: BaseEstimator, TransformerMixin

Transforms a time series into a differentiated time series of order n. It also reverts the differentiation.

Parameters:

Name Type Description Default
order int

Order of differentiation.

 1

Attributes:

Name Type Description
order int

Order of differentiation.
initial_values list

List with the initial value of the time series after each differentiation. This is used to revert the differentiation.
last_values list

List with the last value of the time series after each differentiation. This is used to revert the differentiation of a new window of data. A new window of data is a time series that starts right after the time series used to fit the transformer.
Source code in skforecast\preprocessing\preprocessing.py 
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def __init__(
    self, 
    order: int=1
) -> None:

    if not isinstance(order, int):
        raise TypeError(f"Parameter 'order' must be an integer. Found {type(order)}.")
    if order < 1:
        raise ValueError(f"Parameter 'order' must be an integer greater than 0. Found {order}.")

    self.order = order
    self.initial_values = []
    self.last_values = []

fit(X, y=None)

Fits the transformer. This method only removes the values stored in self.initial_values.

Parameters:

Name Type Description Default
X numpy ndarray

Time series to be differentiated.

 required
y Ignored

Not used, present here for API consistency by convention.

 None

Returns:

Name Type Description
self TimeSeriesDifferentiator
Source code in skforecast\preprocessing\preprocessing.py 
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def fit(
    self, 
    X: np.ndarray, 
    y: Any=None
) -> Self:
    """
    Fits the transformer. This method only removes the values stored in
    `self.initial_values`.

    Parameters
    ----------
    X : numpy ndarray
        Time series to be differentiated.
    y : Ignored
        Not used, present here for API consistency by convention.

    Returns
    -------
    self : TimeSeriesDifferentiator

    """

    self.initial_values = []
    self.last_values = []

    for i in range(self.order):
        if i == 0:
            self.initial_values.append(X[0])
            self.last_values.append(X[-1])
            X_diff = np.diff(X, n=1)
        else:
            self.initial_values.append(X_diff[0])
            self.last_values.append(X_diff[-1])
            X_diff = np.diff(X_diff, n=1)

    return self

transform(X, y=None)

Transforms a time series into a differentiated time series of order n and stores the values needed to revert the differentiation.

Parameters:

Name Type Description Default
X numpy ndarray

Time series to be differentiated.

 required
y Ignored

Not used, present here for API consistency by convention.

 None

Returns:

Name Type Description
X_diff numpy ndarray

Differentiated time series. The length of the array is the same as the original time series but the first n=order values are nan.
Source code in skforecast\preprocessing\preprocessing.py 
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def transform(
    self, 
    X: np.ndarray, 
    y: Any=None
) -> np.ndarray:
    """
    Transforms a time series into a differentiated time series of order n and
    stores the values needed to revert the differentiation.

    Parameters
    ----------
    X : numpy ndarray
        Time series to be differentiated.
    y : Ignored
        Not used, present here for API consistency by convention.

    Returns
    -------
    X_diff : numpy ndarray
        Differentiated time series. The length of the array is the same as
        the original time series but the first n=`order` values are nan.

    """

    X_diff = np.diff(X, n=self.order)

    X_diff = np.append((np.full(shape=self.order, fill_value=np.nan)), X_diff)

    return X_diff

inverse_transform(X, y=None)

Reverts the differentiation. To do so, the input array is assumed to be a differentiated time series of order n that starts right after the the time series used to fit the transformer.

Parameters:

Name Type Description Default
X numpy ndarray

Differentiated time series.

 required
y Ignored

Not used, present here for API consistency by convention.

 None

Returns:

Name Type Description
X_diff numpy ndarray

Reverted differentiated time series.
Source code in skforecast\preprocessing\preprocessing.py 
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def inverse_transform(
    self, 
    X: np.ndarray, 
    y: Any=None
) -> np.ndarray:
    """
    Reverts the differentiation. To do so, the input array is assumed to be
    a differentiated time series of order n that starts right after the
    the time series used to fit the transformer.

    Parameters
    ----------
    X : numpy ndarray
        Differentiated time series.
    y : Ignored
        Not used, present here for API consistency by convention.

    Returns
    -------
    X_diff : numpy ndarray
        Reverted differentiated time series.

    """

    # Remove initial nan values if present
    X = X[np.argmax(~np.isnan(X)):]
    for i in range(self.order):
        if i == 0:
            X_undiff = np.insert(X, 0, self.initial_values[-1])
            X_undiff = np.cumsum(X_undiff, dtype=float)
        else:
            X_undiff = np.insert(X_undiff, 0, self.initial_values[-(i+1)])
            X_undiff = np.cumsum(X_undiff, dtype=float)

    return X_undiff

inverse_transform_next_window(X, y=None)

Reverts the differentiation. The input array x is assumed to be a differentiated time series of order n that starts right after the the time series used to fit the transformer.

Parameters:

Name Type Description Default
X numpy ndarray

Differentiated time series. It is assumed o start right after the time series used to fit the transformer.

 required
y Ignored

Not used, present here for API consistency by convention.

 None

Returns:

Name Type Description
X_undiff numpy ndarray

Reverted differentiated time series.
Source code in skforecast\preprocessing\preprocessing.py 
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def inverse_transform_next_window(
    self,
    X: np.ndarray,
    y: Any=None
)  -> np.ndarray:
    """
    Reverts the differentiation. The input array `x` is assumed to be a 
    differentiated time series of order n that starts right after the
    the time series used to fit the transformer.

    Parameters
    ----------
    X : numpy ndarray
        Differentiated time series. It is assumed o start right after
        the time series used to fit the transformer.
    y : Ignored
        Not used, present here for API consistency by convention.

    Returns
    -------
    X_undiff : numpy ndarray
        Reverted differentiated time series.

    """

    # Remove initial nan values if present
    X = X[np.argmax(~np.isnan(X)):]

    for i in range(self.order):
        if i == 0:
            X_undiff = np.cumsum(X, dtype=float) + self.last_values[-1]
        else:
            X_undiff = np.cumsum(X_undiff, dtype=float) + self.last_values[-(i+1)]

    return X_undiff