PolynomialTrendForecaster

Forecast time series data with a polynomial trend.

Quickstart

from sktime.forecasting.trend import PolynomialTrendForecaster

estimator = PolynomialTrendForecaster(regressor=None, degree=1, with_intercept=True, prediction_intervals=False)

Parameters(4)

regressorsklearn regressor estimator object, default = None

Define the regression model type. If not set, will default to sklearn.linear_model.LinearRegression

degreeint, default = 1

Degree of polynomial function

with_interceptbool, default=True

If true, then include a feature in which all polynomial powers are zero. (i.e. a column of ones, acts as an intercept term in a linear model)

prediction_intervalsbool, default=False

Whether to compute prediction intervals. If True, additional calculations are done during fit to enable prediction intervals to be calculated during predict. The prediction intervals are calculated according to Section 7.9 in [1]. The formulas are standard and are based on an OLS regression model fitted to the data. The formulas in [1] assume a regression with intercept and are modified appropriately if with_intercept is False.

Examples

>>> from sktime.datasets import load_airline
>>> from sktime.forecasting.trend import PolynomialTrendForecaster
>>> y = load_airline ()
>>> forecaster = PolynomialTrendForecaster (degree = 1)
>>> forecaster. fit (y) PolynomialTrendForecaster(
... )
>>> y_pred = forecaster. predict (fh = [1, 2, 3 ])

References

1. [1 ] Hyndman, Rob J., and George Athanasopoulos. Forecasting: principles and practice, 3rd edition. OTexts: Melbourne, Australia. OTexts.com/fpp3.