Feature Engineering

fourier(df, freq, season_length, k, h=0, id_col='unique_id', time_col='ds')

Compute fourier seasonal terms for training and forecasting Parameters:

Name	Type	Description	Default
`df`	`pandas or polars DataFrame`	Dataframe with ids, times and values for the exogenous regressors.	required
`freq`	`str or int`	Frequency of the data. Must be a valid pandas or polars offset alias, or an integer.	required
`season_length`	`int`	Number of observations per unit of time. Ex: 24 Hourly data.	required
`k`	`int`	Maximum order of the fourier terms	required
`h`	`int`	Forecast horizon. Defaults to 0.	`0`
`id_col`	`str`	Column that identifies each serie. Defaults to ‘unique_id’.	`‘unique_id’`
`time_col`	`str`	Column that identifies each timestep, its values can be timestamps or integers. Defaults to ‘ds’.	`‘ds’`

Returns:

Type	Description
`Tuple[DFType, DFType]`	tuple[pandas or polars DataFrame, pandas or polars DataFrame]: A tuple containing the original DataFrame with the computed features and DataFrame with future values.

trend(df, freq, h=0, id_col='unique_id', time_col='ds')

Add a trend column with consecutive integers for training and forecasting Parameters:

Name	Type	Description	Default
`df`	`pandas or polars DataFrame`	Dataframe with ids, times and values for the exogenous regressors.	required
`freq`	`str or int`	Frequency of the data. Must be a valid pandas or polars offset alias, or an integer.	required
`h`	`int`	Forecast horizon. Defaults to 0.	`0`
`id_col`	`str`	Column that identifies each serie. Defaults to ‘unique_id’.	`‘unique_id’`
`time_col`	`str`	Column that identifies each timestep, its values can be timestamps or integers. Defaults to ‘ds’.	`‘ds’`

Returns:

Type	Description
`Tuple[DFType, DFType]`	tuple[pandas or polars DataFrame, pandas or polars DataFrame]: A tuple containing the original DataFrame with the computed features and DataFrame with future values.

time_features(df, freq, features, h=0, id_col='unique_id', time_col='ds')

Compute timestamp-based features for training and forecasting Parameters:

Name	Type	Description	Default
`df`	`pandas or polars DataFrame`	Dataframe with ids, times and values for the exogenous regressors.	required
`freq`	`str or int`	Frequency of the data. Must be a valid pandas or polars offset alias, or an integer.	required
`features`	`list of str or callable`	Features to compute. Can be string aliases of timestamp attributes or functions to apply to the times.	required
`h`	`int`	Forecast horizon. Defaults to 0.	`0`
`id_col`	`str`	Column that identifies each serie. Defaults to ‘unique_id’.	`‘unique_id’`
`time_col`	`str`	Column that identifies each timestep, its values can be timestamps or integers. Defaults to ‘ds’.	`‘ds’`

Returns:

Type	Description
`Tuple[DFType, DFType]`	tuple[pandas or polars DataFrame, pandas or polars DataFrame]: A tuple containing the original DataFrame with the computed features and DataFrame with future values.

future_exog_to_historic(
    df, freq, features, h=0, id_col="unique_id", time_col="ds"
)

Turn future exogenous features into historic by shifting them h steps. Parameters:

Name	Type	Description	Default
`df`	`pandas or polars DataFrame`	Dataframe with ids, times and values for the exogenous regressors.	required
`freq`	`str or int`	Frequency of the data. Must be a valid pandas or polars offset alias, or an integer.	required
`features`	`list of str`	Features to be converted into historic.	required
`h`	`int`	Forecast horizon. Defaults to 0.	`0`
`id_col`	`str`	Column that identifies each serie. Defaults to ‘unique_id’.	`‘unique_id’`
`time_col`	`str`	Column that identifies each timestep, its values can be timestamps or integers. Defaults to ‘ds’.	`‘ds’`

Returns:

Type	Description
`Tuple[DFType, DFType]`	tuple[pandas or polars DataFrame, pandas or polars DataFrame]: A tuple containing the original DataFrame with the computed features and DataFrame with future values.

pipeline(df, features, freq, h=0, id_col='unique_id', time_col='ds')

Compute several features for training and forecasting Parameters:

Name	Type	Description	Default
`df`	`pandas or polars DataFrame`	Dataframe with ids, times and values for the exogenous regressors.	required
`features`	`list of callable`	List of features to compute. Must take only df, freq, h, id_col and time_col (other arguments must be fixed).	required
`freq`	`str or int`	Frequency of the data. Must be a valid pandas or polars offset alias, or an integer.	required
`h`	`int`	Forecast horizon. Defaults to 0.	`0`
`id_col`	`str`	Column that identifies each serie. Defaults to ‘unique_id’.	`‘unique_id’`
`time_col`	`str`	Column that identifies each timestep, its values can be timestamps or integers. Defaults to ‘ds’.	`‘ds’`

Returns:

Type	Description
`Tuple[DFType, DFType]`	tuple[pandas or polars DataFrame, pandas or polars DataFrame]: A tuple containing the original DataFrame with the computed features and DataFrame with future values.