sktime.performance_metrics.forecasting¶
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class
sktime.performance_metrics.forecasting.
MASE
[source]¶ Bases:
sktime.performance_metrics.forecasting._classes.MetricFunctionWrapper
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sktime.performance_metrics.forecasting.
make_forecasting_scorer
(fn, name=None, greater_is_better=False)[source]¶ Factory method for creating metric classes from metric functions
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sktime.performance_metrics.forecasting.
mape_loss
(y_test, y_pred)[source]¶ - Mean absolute percentage error (MAPE)
MAPE output is non-negative floating point where the best value is 0.0. There is no limit on how large the error can be, particulalrly when y_test values are close to zero. In such cases the function returns a large value instead of inf.
- Parameters
y_test (pandas Series of shape = (fh,) where fh is the forecasting horizon) – Ground truth (correct) target values.
y_pred (pandas Series of shape = (fh,)) – Estimated target values.
- Returns
loss – MAPE loss expressed as a fractional number rather than percentage point.
- Return type
Examples
>>> from sklearn.metrics import mean_absolute_error >>> y_test = pd.Series([1, -1, 2]) >>> y_pred = pd.Series([2, -2, 4]) >>> mape_loss(y_test, y_pred) 1.0
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sktime.performance_metrics.forecasting.
mase_loss
(y_test, y_pred, y_train, sp=1)[source]¶ Mean absolute scaled error.
This scale-free error metric can be used to compare forecast methods on a single series and also to compare forecast accuracy between series. This metric is well suited to intermittent-demand series because it never gives infinite or undefined values.
- Parameters
y_test (pandas Series of shape = (fh,) where fh is the forecasting horizon) – Ground truth (correct) target values.
y_pred (pandas Series of shape = (fh,)) – Estimated target values.
y_train (pandas Series of shape = (n_obs,)) – Observed training values.
sp (int) – Seasonal periodicity of training data.
- Returns
loss – MASE loss
- Return type
References
- ..[1] Hyndman, R. J. (2006). “Another look at measures of forecast
accuracy”, Foresight, Issue 4.
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class
sktime.performance_metrics.forecasting.
sMAPE
[source]¶ Bases:
sktime.performance_metrics.forecasting._classes.MetricFunctionWrapper
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sktime.performance_metrics.forecasting.
smape_loss
(y_test, y_pred)[source]¶ Symmetric mean absolute percentage error
- Parameters
y_test (pandas Series of shape = (fh,) where fh is the forecasting horizon) – Ground truth (correct) target values.
y_pred (pandas Series of shape = (fh,)) – Estimated target values.
- Returns
loss – sMAPE loss
- Return type