Feature engineering

Create exogenous regressors for your models

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source

fourier

 fourier
          (df:Union[pandas.core.frame.DataFrame,polars.dataframe.frame.Dat
          aFrame], freq:str, season_length:int, k:int, h:int=0,
          id_col:str='unique_id', time_col:str='ds')

Compute fourier seasonal terms for training and forecasting

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

import pandas as pd

from utilsforecast.data import generate_series

series = generate_series(5, equal_ends=True)
transformed_df, future_df = fourier(series, freq='D', season_length=7, k=2, h=1)
transformed_df

	unique_id	ds	y	sin1_7	sin2_7	cos1_7	cos2_7
0	0	2000-10-05	0.428973	-0.974927	0.433894	-0.222526	-0.900964
1	0	2000-10-06	1.423626	-0.781835	-0.974926	0.623486	-0.222531
2	0	2000-10-07	2.311782	-0.000005	-0.000009	1.000000	1.000000
3	0	2000-10-08	3.192191	0.781829	0.974930	0.623493	-0.222512
4	0	2000-10-09	4.148767	0.974929	-0.433877	-0.222517	-0.900972
…	…	…	…	…	…	…	…
1096	4	2001-05-10	4.058910	-0.974927	0.433888	-0.222523	-0.900967
1097	4	2001-05-11	5.178157	-0.781823	-0.974934	0.623500	-0.222495
1098	4	2001-05-12	6.133142	-0.000002	-0.000003	1.000000	1.000000
1099	4	2001-05-13	0.403709	0.781840	0.974922	0.623479	-0.222548
1100	4	2001-05-14	1.081779	0.974928	-0.433882	-0.222520	-0.900970

future_df

	unique_id	ds	sin1_7	sin2_7	cos1_7	cos2_7
0	0	2001-05-15	0.433871	-0.781813	-0.900975	0.623513
1	1	2001-05-15	0.433871	-0.781813	-0.900975	0.623513
2	2	2001-05-15	0.433871	-0.781813	-0.900975	0.623513
3	3	2001-05-15	0.433871	-0.781813	-0.900975	0.623513
4	4	2001-05-15	0.433871	-0.781813	-0.900975	0.623513

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source

trend

 trend
        (df:Union[pandas.core.frame.DataFrame,polars.dataframe.frame.DataF
        rame], freq:str, h:int=0, id_col:str='unique_id',
        time_col:str='ds')

Add a trend column with consecutive integers for training and forecasting

	Type	Default	Details
df	Union		Dataframe with ids, times and values for the exogenous regressors.
freq	str		Frequency of the data. Must be a valid pandas or polars offset alias, or an integer.
h	int	0	Forecast horizon.
id_col	str	unique_id	Column that identifies each serie.
time_col	str	ds	Column that identifies each timestep, its values can be timestamps or integers.
Returns	Tuple		Original DataFrame with the computed features

series = generate_series(5, equal_ends=True)
transformed_df, future_df = trend(series, freq='D', h=1)
transformed_df

	unique_id	ds	y	trend
0	0	2000-10-05	0.428973	152.0
1	0	2000-10-06	1.423626	153.0
2	0	2000-10-07	2.311782	154.0
3	0	2000-10-08	3.192191	155.0
4	0	2000-10-09	4.148767	156.0
…	…	…	…	…
1096	4	2001-05-10	4.058910	369.0
1097	4	2001-05-11	5.178157	370.0
1098	4	2001-05-12	6.133142	371.0
1099	4	2001-05-13	0.403709	372.0
1100	4	2001-05-14	1.081779	373.0

future_df

	unique_id	ds	trend
0	0	2001-05-15	374.0
1	1	2001-05-15	374.0
2	2	2001-05-15	374.0
3	3	2001-05-15	374.0
4	4	2001-05-15	374.0

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source

pipeline

 pipeline
           (df:Union[pandas.core.frame.DataFrame,polars.dataframe.frame.Da
           taFrame], features:List[Callable], freq:str, h:int=0,
           id_col:str='unique_id', time_col:str='ds')

Compute several features for training and forecasting

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

features = [
    trend,
    partial(fourier, season_length=7, k=1),
    partial(fourier, season_length=28, k=1),
]
transformed_df, future_df = pipeline(
    series,
    features=features,
    freq='D',
    h=1,
)
transformed_df

	unique_id	ds	y	trend	sin1_7	cos1_7	sin1_28	cos1_28
0	0	2000-10-05	0.428973	152.0	-0.974927	-0.222526	0.433885	-9.009683e-01
1	0	2000-10-06	1.423626	153.0	-0.781835	0.623486	0.222522	-9.749276e-01
2	0	2000-10-07	2.311782	154.0	-0.000005	1.000000	0.000001	-1.000000e+00
3	0	2000-10-08	3.192191	155.0	0.781829	0.623493	-0.222520	-9.749281e-01
4	0	2000-10-09	4.148767	156.0	0.974929	-0.222517	-0.433883	-9.009693e-01
…	…	…	…	…	…	…	…	…
1096	4	2001-05-10	4.058910	369.0	-0.974927	-0.222523	0.900969	4.338843e-01
1097	4	2001-05-11	5.178157	370.0	-0.781823	0.623500	0.974929	2.225177e-01
1098	4	2001-05-12	6.133142	371.0	-0.000002	1.000000	1.000000	4.251100e-07
1099	4	2001-05-13	0.403709	372.0	0.781840	0.623479	0.974927	-2.225243e-01
1100	4	2001-05-14	1.081779	373.0	0.974928	-0.222520	0.900969	-4.338835e-01

future_df

	unique_id	ds	trend	sin1_7	cos1_7	sin1_28	cos1_28
0	0	2001-05-15	374.0	0.433871	-0.900975	0.781829	-0.623493
1	1	2001-05-15	374.0	0.433871	-0.900975	0.781829	-0.623493
2	2	2001-05-15	374.0	0.433871	-0.900975	0.781829	-0.623493
3	3	2001-05-15	374.0	0.433871	-0.900975	0.781829	-0.623493
4	4	2001-05-15	374.0	0.433871	-0.900975	0.781829	-0.623493