AutoCorrelationTransformer
Auto-correlation transformer.
The autocorrelation function measures how correlated a timeseries is with itself at different lags. The AutocorrelationTransformer returns these values as a series for each lag up to the n_lags specified.
Schnellstart
from sktime.transformations.acf import AutoCorrelationTransformer
estimator = AutoCorrelationTransformer(adjusted=False, n_lags=None, fft=False, missing='none')Tags
Fähigkeiten
- Reihen ungleicher Länge
- Multivariat: Nicht unterstützt
- Inverse Transformation: Nicht unterstützt
- Fehlende Werte: Nicht unterstützt
- Entfernt fehlende Werte: Nicht unterstützt
- Gleicht Reihenlängen an: Nicht unterstützt
Eigenschaften
- Eingabetypscitype:transform-input
- Series
- Ausgabetypscitype:transform-output
- Series
- Label-Typscitype:transform-labels
- None
- Fit ist leerfit_is_empty
- Ja
- Behält den Zeitindextransform-returns-same-time-index
- Nein
- Benötigt Xrequires_X
- Ja
- Benötigt yrequires_y
- Nein
- X und y brauchen denselben IndexX-y-must-have-same-index
- Nein
Parameter(4)
- adjustedbool, default=False
- If True, then denominators for autocovariance are n-k, otherwise n.
- n_lagsint, default=None
- Number of lags to return autocorrelation for. If None, statsmodels acf function uses min(10 * np.log10(nobs), nobs - 1).
- fftbool, default=False
- If True, computes the ACF via FFT.
- missing{“none”, “raise”, “conservative”, “drop”}, default=”none”
How missing values are to be treated in autocorrelation function calculations.
“none” performs no checks or handling of missing values
“raise” raises an exception if NaN values are found.
“drop” removes the missing observations and then estimates the autocovariances treating the non-missing as contiguous.
“conservative” computes the autocovariance using nan-ops so that nans are removed when computing the mean and cross-products that are used to estimate the autocovariance. “n” in calculation is set to the number of non-missing observations.
Beispiele
>>> from sktime.transformations.acf import AutoCorrelationTransformer
>>> from sktime.datasets import load_airline
>>> y = load_airline ()
>>> transformer = AutoCorrelationTransformer (n_lags = 12)
>>> y_hat = transformer. fit_transform (y)