Forecaster
RecursiveTabularRegressionForecaster
Recursive reduction from forecasting to tabular regression.
For the recursive strategy, a single estimator is fit for a one-step-ahead forecasting horizon and then called iteratively to predict multiple steps ahead.
Schnellstart
python
from sktime.forecasting.compose import RecursiveTabularRegressionForecaster
estimator = RecursiveTabularRegressionForecaster(estimator, window_length=10, transformers=None, pooling='local')Tags
Fähigkeiten
- Kategoriale Merkmale
- Prognoseintervalle
- Fehlende Werte
- Exogene Variablen
- In-Sample-Vorhersagen: Nicht unterstützt
- In-Sample-Prognoseintervalle: Nicht unterstützt
- Multivariat: Nicht unterstützt
Eigenschaften
- Prognosehorizont beim Fit nötigrequires-fh-in-fit
- Nein
- X und y brauchen denselben IndexX-y-must-have-same-index
- Ja
Parameter(4)
- estimatorEstimator
- A tabular regression estimator as provided by scikit-learn.
- window_lengthint, optional (default=10)
- The length of the sliding window used to transform the series into a tabular matrix.
- transformers: list of transformers (default = None)
- A suitable list of transformers that allows for using an en-bloc approach with make_reduction. This means that instead of using the raw past observations of y across the window length, suitable features will be generated directly from the past raw observations. Currently only supports WindowSummarizer (or a list of WindowSummarizers) to generate features e.g. the mean of the past 7 observations.
- pooling: str {“local”, “global”}, optional
- Specifies whether separate models will be fit at the level of each instance (local) of if you wish to fit a single model to all instances (“global”).