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Transformer

PartialAutoCorrelationTransformer

API referenceSource
Unequal length

Partial auto-correlation transformer.

Quickstart

python
from sktime.transformations.acf import PartialAutoCorrelationTransformer

estimator = PartialAutoCorrelationTransformer(n_lags=None, method='ywadjusted')

Parameters(2)

n_lagsint, default=None
Number of lags to return partial autocorrelation for. If None, statsmodels acf function uses min(10 * np.log10(nobs), nobs // 2 - 1).
methodstr, default=”ywadjusted”

Specifies which method for the calculations to use.

  • “yw” or “ywadjusted”: Yule-Walker with sample-size adjustment in denominator for acovf. Default.

  • “ywm” or “ywmle”: Yule-Walker without adjustment.

  • “ols”: regression of time series on lags of it and on constant.

  • “ols-inefficient”: regression of time series on lags using a single common sample to estimate all pacf coefficients.

  • “ols-adjusted”: regression of time series on lags with a bias adjustment.

  • “ld” or “ldadjusted”: Levinson-Durbin recursion with bias correction.

  • “ldb” or “ldbiased”: Levinson-Durbin recursion without bias correction.

Examples

>>> from sktime.transformations.acf import PartialAutoCorrelationTransformer
>>> from sktime.datasets import load_airline
>>> y = load_airline ()
>>> transformer = PartialAutoCorrelationTransformer (n_lags = 12)
>>> y_hat = transformer. fit_transform (y)