class gtime.forecasting.TrendForecaster(trend: str, trend_x0: numpy.array, loss: Callable = <function mean_squared_error>, method: str = 'BFGS')

Trend forecasting model.

This estimator optimizes a trend function on train data and will forecast using this trend function with optimized parameters.

trend"polynomial" | "exponential", required

The kind of trend removal to apply.

trend_x0np.array, required

Initialisation parameters passed to the trend function

lossCallable, optional, default: mean_squared_error

Loss function to minimize.

methodstr, optional, default: "BFGS"

Loss function optimisation method


>>> import pandas as pd
>>> import numpy as np
>>> from gtime.model_selection import horizon_shift, FeatureSplitter
>>> from gtime.forecasting import TrendForecaster
>>> X = pd.DataFrame(np.random.random((10, 1)), index=pd.date_range("2020-01-01", "2020-01-10"))
>>> y = horizon_shift(X, horizon=2)
>>> X_train, y_train, X_test, y_test = FeatureSplitter().transform(X, y)
>>> tf = TrendForecaster(trend='polynomial', trend_x0=np.zeros(2))


fit(self, X[, y])

Fit the estimator.

get_params(self[, deep])

Get parameters for this estimator.

predict(self, X)

Using the fitted polynomial, predict the values starting from X.

score(self, X, y[, sample_weight])

Return the coefficient of determination R^2 of the prediction.

set_params(self, \*\*params)

Set the parameters of this estimator.

__init__(self, trend:str, trend_x0:<built-in function array>, loss:Callable=<function mean_squared_error at 0x12814f620>, method:str='BFGS')

Initialize self. See help(type(self)) for accurate signature.

fit(self, X:pandas.core.frame.DataFrame, y=None) → 'TrendForecaster'

Fit the estimator.

Xpd.DataFrame, shape (n_samples, n_features), required

Input data.


There is no need of a target in a transformer, yet the pipeline API requires this parameter.


Returns self.

get_params(self, deep=True)

Get parameters for this estimator.

deepbool, default=True

If True, will return the parameters for this estimator and contained subobjects that are estimators.

paramsmapping of string to any

Parameter names mapped to their values.

predict(self, X:pandas.core.frame.DataFrame) → pandas.core.frame.DataFrame

Using the fitted polynomial, predict the values starting from X.

X: pd.DataFrame, shape (n_samples, 1), required

The time series on which to predict.

predictionspd.DataFrame, shape (n_samples, 1)

The output predictions.


Raised if the model is not fitted yet.

score(self, X, y, sample_weight=None)

Return the coefficient of determination R^2 of the prediction.

The coefficient R^2 is defined as (1 - u/v), where u is the residual sum of squares ((y_true - y_pred) ** 2).sum() and v is the total sum of squares ((y_true - y_true.mean()) ** 2).sum(). The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a R^2 score of 0.0.

Xarray-like of shape (n_samples, n_features)

Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead, shape = (n_samples, n_samples_fitted), where n_samples_fitted is the number of samples used in the fitting for the estimator.

yarray-like of shape (n_samples,) or (n_samples, n_outputs)

True values for X.

sample_weightarray-like of shape (n_samples,), default=None

Sample weights.


R^2 of self.predict(X) wrt. y.


The R2 score used when calling score on a regressor will use multioutput='uniform_average' from version 0.23 to keep consistent with r2_score. This will influence the score method of all the multioutput regressors (except for MultiOutputRegressor). To specify the default value manually and avoid the warning, please either call r2_score directly or make a custom scorer with make_scorer (the built-in scorer 'r2' uses multioutput='uniform_average').

set_params(self, **params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.


Estimator parameters.


Estimator instance.