gluonts.evaluation.metrics module#
- gluonts.evaluation.metrics.abs_error(target: numpy.ndarray, forecast: numpy.ndarray) float[source]#
Absolute error.
\[abs\_error = sum(|Y - \hat{Y}|)\]
- gluonts.evaluation.metrics.abs_target_mean(target) float[source]#
Absolute target mean.
\[abs\_target\_mean = mean(|Y|)\]
- gluonts.evaluation.metrics.abs_target_sum(target) float[source]#
Absolute target sum.
\[abs\_target\_sum = sum(|Y|)\]
- gluonts.evaluation.metrics.calculate_seasonal_error(past_data: numpy.ndarray, freq: Optional[str] = None, seasonality: Optional[int] = None)[source]#
- \[seasonal\_error = mean(|Y[t] - Y[t-m]|)\]
where m is the seasonal frequency. See [HA21] for more details.
- gluonts.evaluation.metrics.coverage(target: numpy.ndarray, forecast: numpy.ndarray) float[source]#
coverage.
\[coverage = mean(Y <= \hat{Y})\]
- gluonts.evaluation.metrics.mape(target: numpy.ndarray, forecast: numpy.ndarray) float[source]#
- \[mape = mean(|Y - \hat{Y}| / |Y|))\]
See [HA21] for more details.
- gluonts.evaluation.metrics.mase(target: numpy.ndarray, forecast: numpy.ndarray, seasonal_error: float) float[source]#
- \[mase = mean(|Y - \hat{Y}|) / seasonal\_error\]
See [HA21] for more details.
- gluonts.evaluation.metrics.mse(target: numpy.ndarray, forecast: numpy.ndarray) float[source]#
- \[mse = mean((Y - \hat{Y})^2)\]
See [HA21] for more details.
- gluonts.evaluation.metrics.msis(target: numpy.ndarray, lower_quantile: numpy.ndarray, upper_quantile: numpy.ndarray, seasonal_error: float, alpha: float) float[source]#
- \[msis = mean(U - L + 2/alpha * (L-Y) * I[Y<L] + 2/alpha * (Y-U) * I[Y>U]) / seasonal\_error\]
See [SSA20] for more details.
- gluonts.evaluation.metrics.num_masked_values(target) float[source]#
Count number of masked values in target.