Transformer
ConditionalDeseasonalizer
Inverse transformUnequal-length series
Remove seasonal components from time series, conditional on seasonality test.
Fit tests for seasonality and if the passed time series has a seasonal component it applies seasonal decomposition provided by statsmodels to compute the seasonal component. If the test is negative seasonal_ is set to all ones (if model is “multiplicative”) or to all zeros (if model is “additive”).
Transform aligns seasonal components stored in seasonal_ with the time index of the passed series and then subtracts them (“additive” model) from the passed series or divides the passed series by them (“multiplicative” model).
Quickstart
python
from sktime.transformations.detrend import ConditionalDeseasonalizer
estimator = ConditionalDeseasonalizer(seasonality_test=None, sp=1, model='additive')Tags
Capabilities
- Inverse transform: Supported
- Unequal-length series
- Multivariate: Not supported
- Missing values: Not supported
- Removes missing values: Not supported
- Equalizes series length: Not supported
Properties
- Input typescitype:transform-input
- Series
- Output typescitype:transform-output
- Series
- Label typescitype:transform-labels
- None
- Fit is emptyfit_is_empty
- No
- Keeps the time indextransform-returns-same-time-index
- Yes
- Requires Xrequires_X
- Yes
- Requires yrequires_y
- No
- X and y need the same indexX-y-must-have-same-index
- No
Parameters(3)
- seasonality_testcallable or None, default=None
- Callable that tests for seasonality and returns True when data is seasonal and False otherwise. If None, 90% autocorrelation seasonality test is used.
- spint, default=1
- Seasonal periodicity.
- model{“additive”, “multiplicative”}, default=”additive”
- Model to use for estimating seasonal component.
Examples
>>> from sktime.transformations.detrend import ConditionalDeseasonalizer
>>> from sktime.datasets import load_airline
>>> y = load_airline ()
>>> transformer = ConditionalDeseasonalizer (sp = 12)
>>> y_hat = transformer. fit_transform (y)