Forecaster
RecursiveTimeSeriesRegressionForecaster
Recursive reduction from forecasting to time series 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.
Quickstart
python
from sktime.forecasting.compose import RecursiveTimeSeriesRegressionForecaster
estimator = RecursiveTimeSeriesRegressionForecaster(estimator, window_length=10, transformers=None, pooling='local')Tags
Capabilities
- Categorical features
- Prediction intervals
- Missing values
- Exogenous variables
- In-sample predictions: Not supported
- In-sample prediction intervals: Not supported
- Multivariate: Not supported
Properties
- Needs forecast horizon in fitrequires-fh-in-fit
- No
- X and y need the same indexX-y-must-have-same-index
- Yes
Parameters(2)
- estimatorEstimator
- A time-series regression estimator as provided by sktime.
- window_lengthint, optional (default=10)
- The length of the sliding window used to transform the series into a tabular matrix.