# pastas.stats.metrics.aic#

aic(obs=None, sim=None, res=None, missing='drop', nparam=1)[source]#

Compute the Akaike Information Criterium (AIC).

Parameters
• obs (pandas.Series) – Series with the observed values.

• sim (pandas.Series) – Series with the simulated values.

• res (pandas.Series) – Series with the residual values. If time series for the residuals are provided, the sim and obs arguments are ignored.

• nparam (int, optional) – number of calibrated parameters.

• missing (str, optional) – string with the rule to deal with missing values. Only “drop” is supported now.

Notes

The Akaike Information Criterium (AIC) is computed as follows:

$\text{AIC} = -2 log(L) + 2 nparam$

where $$n_{param}$$ is the number of calibration parameters and L is the likelihood function for the model.