pastas.stats.metrics.me#

pastas.stats.metrics.me(obs: pandas.Series | None = None, sim: pandas.Series | None = None, res: pandas.Series | None = None, missing: str = 'drop', weighted: bool = False, max_gap: int = 30) → float#

Compute the (weighted) Mean Error (ME).

Parameters:

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

  • obs (pandas.Series, optional) – The Series with the observed 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) – Weight the values by the normalized time step to account for irregular time series. 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.

Notes

The Mean Error (ME) between the observed (\(y_o\)) and simulated (\(y_s\)) time series is computed as follows:

\[\text{ME} = \sum_{i=1}^{N} w_i (y_o - y_s)\]

where \(N\) is the number of observations in the observed time series.