pmdarima.preprocessing.FourierFeaturizer

class pmdarima.preprocessing.FourierFeaturizer(m, k=None, prefix=None)[source][source]

Fourier terms for modeling seasonality

This transformer creates an exogenous matrix containing terms from a Fourier series, up to order k. It is based on R::forecast code [1]. In practice, it permits us to fit a seasonal time series without seasonal order (i.e., seasonal=False) by supplying decomposed seasonal Fourier terms as an exogenous array.

The advantages of this technique, per Hyndman [2]:

  • It allows any length seasonality

  • The seasonal pattern is smooth for small values of K (but more wiggly seasonality can be handled by increasing K)

  • The short-term dynamics are easily handled with a simple ARMA error

The disadvantage is that the seasonal periodicity of the time series is assumed to be fixed.

Functionally, this is a featurizer. This means that exogenous features are derived from y, as opposed to transforming an existing exog array. It also behaves slightly differently in the transform() stage than most other exogenous transformers in that exog is not a required arg, and it takes **kwargs. See the transform() docstr for more info.

Parameters:

m : int

The seasonal periodicity of the endogenous vector, y.

k : int, optional (default=None)

The number of sine and cosine terms (each) to include. I.e., if k is 2, 4 new features will be generated. k must not exceed m/2, which is the default value if not set. The value of k can be selected by minimizing the AIC.

prefix : str or None, optional (default=None)

The feature prefix

Notes

  • Helpful for long seasonal periods (large m) where seasonal=True seems to take a very long time to fit a model.

References

Examples

>>> import pandas as pd
>>> from pmdarima.preprocessing import FourierFeaturizer
>>> from pmdarima.datasets import load_wineind
>>> y = load_wineind()
>>> trans = FourierFeaturizer(12, 4)
>>> y_prime, X = trans.fit_transform(y)
>>> X.head()
   FOURIER_S12-0     FOURIER_C12-0    ...     FOURIER_S12-3  FOURIER_C12-3
0       0.500000      8.660254e-01    ...      8.660254e-01           -0.5
1       0.866025      5.000000e-01    ...     -8.660255e-01           -0.5
2       1.000000     -4.371139e-08    ...      1.748456e-07            1.0
3       0.866025     -5.000001e-01    ...      8.660253e-01           -0.5
4       0.500000     -8.660254e-01    ...     -8.660255e-01           -0.5

Methods

fit(y[, X])

Fit the transformer

fit_transform(y[, X])

Fit and transform the arrays

get_metadata_routing()

Get metadata routing of this object.

get_params([deep])

Get parameters for this estimator.

set_output(*[, transform])

Set output container.

set_params(**params)

Set the parameters of this estimator.

set_transform_request(*[, n_periods])

Request metadata passed to the transform method.

transform(y[, X, n_periods])

Create Fourier term features

update_and_transform(y[, X])

Update the params and return the transformed arrays
__init__(m, k=None, prefix=None)[source][source]

Examples using pmdarima.preprocessing.FourierFeaturizer

Pipelines with auto_arima

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