CurveFitForecaster

class CurveFitForecaster(function, curve_fit_params=None, origin='unix_zero', normalise_index=False)[source]

The CurveFitForecaster takes a function and fits it by using scipy curve_fit.

The CurveFitForecaster uses the scipy curve_fit method to determine the optimal parameters for a given function.

If the index is an integer index, it directly uses the index values. If the index is a pd.DatetimeIndex or a pd.PeriodIndex, the index values are transformed into floats using two distinct approaches:

For a pd.DatetimeIndex, it calculates the number of days since 1970-01-01. For a pd.PeriodIndex, it computes the number of (full) periods since

1970-01-01.
For a pd.DatetimeIndex, it calculates the number of days since the first index value. For a pd.PeriodIndex, it calculates or the number of (dull) periods since the first index value.

Furthermore, the difference between the index values can be normalised by setting the difference between the first and the second index value to one.

In fit 1. The index of the input time series is transformed to a list of floats. 2. The scipy curve_fit is called using the list of floats as x values,

and the time series values as y values.

In predict 1. The ForecastingHorizon is transformed to a list of floats. 2. The list of floats is passed together with the fitted parameters to the

function to provide the forecast.

Parameters:

function: Callable[[Iterable[float], …], Iterable[float]]: The function that should be fitted and used to make forecasts. The signature of the functions is function(x, ...). It takes the independent variables as first argument and the parameters to fit as separate remaining arguments. See scipy.optimize.curve_fit for more information.
curve_fit_params: dict, default=None: Additional parameters that should be passed to the curve_fit method. See scipy.optimize.curve_fit for more information.
origin: {“unix_zero”, “first_index”}, default=”unix_zero”: The origin of the time series index.
normalise_index: bool, default=False: If True, the differences between the index values are normalised by setting the difference between the first and second index value to one.

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.forecasting.trend import CurveFitForecaster
>>> from sktime.datasets import load_airline
>>> y = load_airline()
>>> def linear_function(x, a, b):
...     return a * x + b
>>> forecaster = CurveFitForecaster(function=linear_function,
...                                 curve_fit_params={"p0":[-1, 1]})
>>> forecaster.fit(y)
CurveFitForecaster(...)
>>> 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.