metrics

Implementation of metrics for (mulit-horizon) timeseries forecasting.

Classes

AggregationMetric(metric, **kwargs)	Calculate metric on mean prediction and actuals.
BetaDistributionLoss([name, quantiles, ...])	Beta distribution loss for unit interval data.
CompositeMetric([metrics, weights])	Metric that combines multiple metrics.
CrossEntropy([reduction])	Cross entropy loss for classification.
DistributionLoss([name, quantiles, reduction])	DistributionLoss base class.
LogNormalDistributionLoss([name, quantiles, ...])	Log-normal loss.
MAE([reduction])	Mean average absolute error.
MAPE([reduction])	Mean absolute percentage.
MASE([reduction])	Mean absolute scaled error
Metric([name, quantiles, reduction])	Base metric class that has basic functions that can handle predicting quantiles and operate in log space.
MultiHorizonMetric([reduction])	Abstract class for defining metric for a multihorizon forecast
MultiLoss(metrics[, weights])	Metric that can be used with muliple metrics.
NegativeBinomialDistributionLoss([name, ...])	Negative binomial loss, e.g.
NormalDistributionLoss([name, quantiles, ...])	Normal distribution loss.
PoissonLoss([reduction])	Poisson loss for count data
QuantileLoss([quantiles])	Quantile loss, i.e. a quantile of q=0.5 will give half of the mean absolute error as it is calcualted as.
RMSE([reduction])	Root mean square error
SMAPE([reduction])	Symmetric mean absolute percentage.

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