ArpsHarmonic

class ArpsHarmonic(qi_init=None, di_init=0.1, n_samples=1000, random_state=None, output='rate', anchor=False, base_np=0.0, max_fit_retries=3)[source]

Arps harmonic decline curve forecaster.

Special case of hyperbolic decline with b = 1:

\[\begin{split}q(t) = \\frac{q_i}{1 + D_i t}, \\quad N_p(t) = \\frac{q_i}{D_i} \\ln(1 + D_i t)\end{split}\]

Parameters qi and Di are estimated via nonlinear least squares (scipy.optimize.curve_fit). Probabilistic forecasts are produced by Monte Carlo sampling from the multivariate normal distribution over the fitted parameters using the covariance matrix from the curve fit.

Parameters:

qi_initfloat or None, default=None: Initial rate guess. If None, the first observed value is used.
di_initfloat, default=0.1: Initial nominal decline rate guess.
n_samplesint, default=1000: Number of Monte Carlo samples for probabilistic forecasts.
random_stateint, RandomState instance or None, default=None: Seed for reproducible probabilistic forecasts.
outputstr, default=”rate”: "rate" for instantaneous rate, "cumulative" for cumulative production.
anchorstr or False, default=False: Last-observation anchoring: "multiplicative", "additive", or False.
base_npfloat, default=0.0: Cumulative production offset added when output="cumulative".
max_fit_retriesint, default=3: Number of additional fitting attempts with perturbed initial parameters if the first attempt fails to converge. Set to 0 to disable retries. If all attempts fail, naive (degenerate) prediction intervals are returned with a warning.

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

>>> import pandas as pd
>>> import numpy as np
>>> from sktime.forecasting.arps_dca import ArpsHarmonic
>>> t = np.arange(10)
>>> q = 1000 / (1 + 0.1 * t)
>>> y = pd.Series(q, index=t)
>>> forecaster = ArpsHarmonic()
>>> forecaster.fit(y, fh=[1, 2, 3])
ArpsHarmonic(...)

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.