pastas.noisemodels.ArNoiseModel#

class pastas.noisemodels.ArNoiseModel(name: str = 'noise', norm: bool = True)#

Noise model with exponential decay of the residuals and weighting.

Parameters:

  • name (str, optional) – Name of the noise model. Default is “noise”.

  • norm (boolean, optional) – Boolean to indicate whether weights are normalized according to the Von Asmuth and Bierkens (2005) paper. Default is True.

Notes

Calculates the noise von Asmuth and Bierkens [2005] according to:

\[v(t_1) = r(t_1) - r(t_0) * \exp(- \Delta t / \alpha)\]

Calculates the weights as

\[w = 1 / \sqrt{(1 - \exp(-2 \Delta t / \alpha))}\]

The units of the alpha parameter is always in days. The first value of the noise is the residual (\(v(t=0=r(t=0)\)). First weight is 1 / sig_residuals (i.e., delt = infty). Normalization of weights as in von Asmuth and Bierkens [2005], optional.

property nparam: int#

Number of parameters of the noise model.

Methods#

set_init_parameters(→ None)

Set initial noise model parameters.

simulate(→ pandas.Series)

Simulate noise from the residuals.

weights(→ pandas.Series)

Method to calculate the weights for the noise.

get_correction(→ pandas.Series)

Get the correction for a forecast using the noise model.

to_dict(→ dict)

Method to return a dict to store the noise model