pastas.stats.metrics.nnse#

nnse(obs, sim=None, res=None, missing='drop', weighted=False, max_gap=30)[source]#

Compute the (weighted) Normalized Nash-Sutcliffe Efficiency (NNSE).

Parameters

obs (pandas.Series) – Series with the observed values.
sim (pandas.Series, optional) – The Series with the simulated values.
res (pandas.Series, optional) – The Series with the residual values. If time series for the residuals are provided, the sim and obs arguments are ignored. Note that the residuals must be computed as obs - sim here.
missing (str, optional) – string with the rule to deal with missing values. Only “drop” is supported now.
weighted (bool, optional) – If weighted is True, the variances are computed using the time step between observations as weights. Default is False.
max_gap (int, optional) – maximum allowed gap period in days to use for the computation of the weights. All time steps larger than max_gap are replace with the max_gap value. Default value is 30 days.

Return type

float

Notes

NNSE computed according to

\[\text{NNSE} = 1 / (2 - NSE)\]

This metric normalizes the NSE between ~0 and 1 instead of -infinity and 1. So the optimal value for NNSE is 1, same as the NSE. However, an NNSE value of 0.5 corresponds to an NSE of 0, while the worst possible NNSE value is ~0.