# Transformations ------------------------------------------------------------------------ source ### box_cox_transform ``` python def box_cox_transform( x, # The input data to be transformed. shift:bool=False, # Whether to shift the data by 1 before transformation to handle zeros. box_cox_lmda:float=None, # The lambda parameter for the Box-Cox transformation. If None, it will be estimated from the data. ): # The Box-Cox transformed data and the lambda used for transformation. ``` *Applies a Box-Cox transformation to a series x.*
Type Default Details
x The input data to be transformed.
shift bool False Whether to shift the data by 1 before transformation to handle zeros.
box_cox_lmda float None The lambda parameter for the Box-Cox transformation. If None, it will be estimated from the data.
Returns The Box-Cox transformed data and the lambda used for transformation.
------------------------------------------------------------------------ source ### back_box_cox_transform ``` python def back_box_cox_transform( y_pred:np.ndarray, # The Box-Cox transformed forecast to be back-transformed. lmda:float, # The lambda parameter used in the Box-Cox transformation. shift:bool=False, # Whether the original data was shifted by 1 before transformation. box_cox_biasadj:bool=False, # Whether to apply bias adjustment to the back-transformed forecast. )->np.ndarray: # The back-transformed forecast. ``` *Inverse Box-Cox transform.*
Type Default Details
y_pred np.ndarray The Box-Cox transformed forecast to be back-transformed.
lmda float The lambda parameter used in the Box-Cox transformation.
shift bool False Whether the original data was shifted by 1 before transformation.
box_cox_biasadj bool False Whether to apply bias adjustment to the back-transformed forecast.
Returns np.ndarray The back-transformed forecast.
------------------------------------------------------------------------ source ### fourier_terms ``` python def fourier_terms( index:Union[pd.Index, tuple], # Either a pandas Index directly (recommended), or a (start, end) tuple of integers or datetime strings. period:Union[int, float], # The period of the seasonality (e.g., 365.25/7 for weekly yearly seasonality). num_terms:int, # The number of Fourier term pairs (sin + cos) to generate. frequency:Optional[str]=None, # Frequency string (e.g., "W-SAT", "D", "M", "W"). Only relevant when index is a (start, end) tuple. t_start:Optional[int]=None, # Starting position of t. Only used when index is a (start, end) tuple. Use len(train_index) to ensure continuity between train and test. )->pd.DataFrame: # DataFrame of Fourier terms aligned to the provided index. ``` *Generate Fourier terms for a given index or (start, end) tuple.*
Type Default Details
index Union[pd.Index, tuple] Either a pandas Index directly (recommended), or a (start, end) tuple of integers or datetime strings.
period Union[int, float] The period of the seasonality (e.g., 365.25/7 for weekly yearly seasonality).
num_terms int The number of Fourier term pairs (sin + cos) to generate.
frequency Optional[str] None Frequency string (e.g., “W-SAT”, “D”, “M”, “W”). Only relevant when index is a (start, end) tuple.
t_start Optional[int] None Starting position of t. Only used when index is a (start, end) tuple. Use len(train_index) to ensure continuity between train and test.
Returns pd.DataFrame DataFrame of Fourier terms aligned to the provided index.
------------------------------------------------------------------------ source ### rolling_mean ``` python def rolling_mean( window_size:int, # The size of the rolling window. shift:int=1, # The number of periods to shift the data before applying the rolling mean (default is 1). min_samples:int=1, # The minimum number of observations in the window required to have a value (default is 1). ): ``` *A class to compute the rolling mean of a time series with specified window size and shift.*
Type Default Details
window_size int The size of the rolling window.
shift int 1 The number of periods to shift the data before applying the rolling mean (default is 1).
min_samples int 1 The minimum number of observations in the window required to have a value (default is 1).
Returns
------------------------------------------------------------------------ source ### rolling_quantile ``` python def rolling_quantile( window_size:int, # The size of the rolling window. quantile:float, # The quantile to compute (between 0 and 1). shift:int=1, # The number of periods to shift the data before applying the rolling quantile (default is 1). min_samples:int=1, # The minimum number of observations in the window required to have a value (default is 1). ): ``` *A class to compute the rolling quantile of a time series with specified window size, quantile, and shift.*
Type Default Details
window_size int The size of the rolling window.
quantile float The quantile to compute (between 0 and 1).
shift int 1 The number of periods to shift the data before applying the rolling quantile (default is 1).
min_samples int 1 The minimum number of observations in the window required to have a value (default is 1).
Returns
------------------------------------------------------------------------ source ### rolling_std ``` python def rolling_std( window_size:int, # The size of the rolling window. shift:int=1, # The number of periods to shift the data before applying the rolling standard deviation (default is 1). min_samples:int=1, # The minimum number of observations in the window required to have a value (default is 1). ): ``` *A class to compute the rolling standard deviation of a time series with specified window size and shift.*
Type Default Details
window_size int The size of the rolling window.
shift int 1 The number of periods to shift the data before applying the rolling standard deviation (default is 1).
min_samples int 1 The minimum number of observations in the window required to have a value (default is 1).
Returns
------------------------------------------------------------------------ source ### rolling_min ``` python def rolling_min( window_size:int, # The size of the rolling window. shift:int=1, # The number of periods to shift the data before applying the rolling minimum (default is 1). min_samples:int=1, # The minimum number of observations in the window required to have a value (default is 1). ): ``` *A class to compute the rolling minimum of a time series with specified window size and shift.*
Type Default Details
window_size int The size of the rolling window.
shift int 1 The number of periods to shift the data before applying the rolling minimum (default is 1).
min_samples int 1 The minimum number of observations in the window required to have a value (default is 1).
Returns
------------------------------------------------------------------------ source ### rolling_max ``` python def rolling_max( window_size:int, # The size of the rolling window. shift:int=1, # The number of periods to shift the data before applying the rolling maximum (default is 1). min_samples:int=1, # The minimum number of observations in the window required to have a value (default is 1). ): ``` *A class to compute the rolling maximum of a time series with specified window size and shift.*
Type Default Details
window_size int The size of the rolling window.
shift int 1 The number of periods to shift the data before applying the rolling maximum (default is 1).
min_samples int 1 The minimum number of observations in the window required to have a value (default is 1).
Returns
------------------------------------------------------------------------ source ### expanding_mean ``` python def expanding_mean( shift:int=1 ): ``` *A class to compute the expanding mean of a time series with specified shift.*
Type Default Details
shift int 1
Returns
------------------------------------------------------------------------ source ### expanding_std ``` python def expanding_std( shift:int=1, # The number of periods to shift the data before applying the expanding standard deviation (default is 1). ): ``` *A class to compute the expanding standard deviation of a time series with specified shift.*
Type Default Details
shift int 1 The number of periods to shift the data before applying the expanding standard deviation (default is 1).
Returns
------------------------------------------------------------------------ source ### expanding_quantile ``` python def expanding_quantile( shift:int=1, # The number of periods to shift the data before applying the expanding quantile (default is 1). quantile:float=0.5, # The quantile to compute (between 0 and 1) (default is 0.5). ): ``` *A class to compute the expanding quantile of a time series with specified shift and quantile.*
Type Default Details
shift int 1 The number of periods to shift the data before applying the expanding quantile (default is 1).
quantile float 0.5 The quantile to compute (between 0 and 1) (default is 0.5).
Returns