SplineTrendForecaster

class SplineTrendForecaster(regressor=None, n_knots=5, degree=1, knots='uniform', extrapolation='constant', with_intercept=True)[source]

Forecast time series data with a spline trend.

Uses an sklearn regressor specified by the regressor parameter to perform regression on time series values against their corresponding indices, after transformation of the indices with SplineTransformer.

Parameters:

regressorsklearn regressor estimator object, default=None

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

n_knotsint, default=5

Number of knots of the splines if knots is one of {‘uniform’, ‘quantile’}. Must be at least 2. Ignored if knots is array-like.

degreeint, default=1

Degree of the splines (1 for linear, 2 for quadratic, etc.).

n_knotsint, default=4

Number of knots for the spline transformation.

knots{‘uniform’, ‘quantile’}or array-like of shape (n_knots, n_features),

default=’uniform’ Determines knot positions such that first knot <= features <= last knot.

‘uniform’: n_knots are distributed uniformly between the

min and max values of the features. - ‘quantile’: n_knots are distributed uniformly along the quantiles of the features. - array-like: Specifies sorted knot positions, including the boundary knots. Internally, additional knots are added before the first knot and after the last knot based on the spline degree.

extrapolation{‘error’, ‘constant’, ‘linear’, ‘continue’, ‘periodic’},

default=’constant’ Determines how to handle values outside the min and max values of the training features:

‘error’: Raises a ValueError.
‘constant’: Uses the spline value at the minimum or maximum feature as

constant extrapolation. - ‘linear’: Applies linear extrapolation. - ‘continue’: Extrapolates as is (equivalent to extrapolate=True in scipy.interpolate.BSpline). - ‘periodic’: Uses periodic splines with a periodicity equal to the distance between the first and last knot, enforcing equal function values and derivatives at these knots.

with_interceptbool, default=True

If True, includes a feature in which all polynomial powers are zero (i.e., a column of ones, acting as an intercept term in a linear model).

Attributes:

cutoff: Cut-off = “present time” state of forecaster.
fh: Forecasting horizon that was passed.
is_fitted: Whether fit has been called.
state: State of the estimator.

Examples

>>> from sktime.datasets import load_airline
>>> from sktime.forecasting.trend import SplineTrendForecaster
>>> y = load_airline()
>>> forecaster = SplineTrendForecaster(
...     n_knots=5,
...     degree=2,
...     knots="uniform",
...     extrapolation="constant"
... )
>>> forecaster.fit(y)
SplineTrendForecaster(...)
>>> y_pred = forecaster.predict(fh=[1, 2, 3])

Methods

`check_is_fitted`([method_name])	Check if the estimator has been fitted.
`clone`()	Obtain a clone of the object with same hyper-parameters and config.
`clone_tags`(estimator[, tag_names])	Clone tags from another object as dynamic override.
`create_test_instance`([parameter_set])	Construct an instance of the class, using first test parameter set.
`create_test_instances_and_names`([parameter_set])	Create list of all test instances and a list of names for them.
`fit`(y[, X, fh])	Fit forecaster to training data.
`fit_predict`(y[, X, fh, X_pred])	Fit and forecast time series at future horizon.
`get_class_tag`(tag_name[, tag_value_default])	Get class tag value from class, with tag level inheritance from parents.
`get_class_tags`()	Get class tags from class, with tag level inheritance from parent classes.
`get_config`()	Get config flags for self.
`get_fitted_params`([deep])	Get fitted parameters.
`get_param_defaults`()	Get object's parameter defaults.
`get_param_names`([sort])	Get object's parameter names.
`get_params`([deep])	Get a dict of parameters values for this object.
`get_pretrained_params`([deep])	Get pretrained parameters of this estimator.
`get_tag`(tag_name[, tag_value_default, ...])	Get tag value from instance, with tag level inheritance and overrides.
`get_tags`()	Get tags from instance, with tag level inheritance and overrides.
`get_test_params`([parameter_set])	Return testing parameter settings for the estimator.
`is_composite`()	Check if the object is composed of other BaseObjects.
`load_from_path`(serial)	Load object from file location.
`load_from_serial`(serial)	Load object from serialized memory container.
`predict`([fh, X])	Forecast time series at future horizon.
`predict_interval`([fh, X, coverage])	Compute/return prediction interval forecasts.
`predict_proba`([fh, X, marginal])	Compute/return fully probabilistic forecasts.
`predict_quantiles`([fh, X, alpha])	Compute/return quantile forecasts.
`predict_residuals`([y, X])	Return residuals of time series forecasts.
`predict_var`([fh, X, cov])	Compute/return variance forecasts.
`pretrain`(y[, X, fh])	Pre-train forecaster on panel (global) data.
`reset`()	Reset the object to a clean post-init state.
`save`([path, serialization_format])	Save serialized self to bytes-like object or to (.zip) file.
`score`(y[, X, fh])	Scores forecast against ground truth, using MAPE (non-symmetric).
`set_config`(**config_dict)	Set config flags to given values.
`set_params`(**params)	Set the parameters of this object.
`set_random_state`([random_state, deep, ...])	Set random_state pseudo-random seed parameters for self.
`set_tags`(**tag_dict)	Set instance level tag overrides to given values.
`update`(y[, X, update_params])	Update cutoff value and, optionally, fitted parameters.
`update_predict`(y[, cv, X, update_params, ...])	Make predictions and update model iteratively over the test set.
`update_predict_single`([y, fh, X, update_params])	Update model with new data and make forecasts.