gluonts.ev.metrics module

class gluonts.ev.metrics.Coverage(q: float)

Bases: object

q: float

class gluonts.ev.metrics.MAECoverage(quantile_levels: Collection[float])

Bases: object

static mean(quantile_levels: Collection[float], **coverages: numpy.ndarray) → numpy.ndarray

quantile_levels: Collection[float]

class gluonts.ev.metrics.MAPE(forecast_type: str = '0.5')

Bases: object

Mean Absolute Percentage Error

forecast_type: str = '0.5'

class gluonts.ev.metrics.MASE(forecast_type: str = '0.5')

Bases: object

Mean Absolute Scaled Error

forecast_type: str = '0.5'

class gluonts.ev.metrics.MSE(forecast_type: str = 'mean')

Bases: object

Mean Squared Error

forecast_type: str = 'mean'

class gluonts.ev.metrics.MSIS(alpha: float = 0.05)

Bases: object

Mean Scaled Interval Score

alpha: float = 0.05

class gluonts.ev.metrics.MeanSumQuantileLoss(quantile_levels: Collection[float])

Bases: object

static mean(**quantile_losses: numpy.ndarray) → numpy.ndarray

quantile_levels: Collection[float]

class gluonts.ev.metrics.MeanWeightedSumQuantileLoss(quantile_levels: Collection[float])

Bases: object

static mean(**quantile_losses: numpy.ndarray) → numpy.ndarray

quantile_levels: Collection[float]

class gluonts.ev.metrics.Metric(*args, **kwds)

Bases: typing_extensions.Protocol

class gluonts.ev.metrics.ND(forecast_type: str = '0.5')

Bases: object

Normalized Deviation

forecast_type: str = '0.5'

static normalized_deviation(sum_absolute_error: numpy.ndarray, sum_absolute_label: numpy.ndarray) → numpy.ndarray

class gluonts.ev.metrics.NRMSE(forecast_type: str = 'mean')

Bases: object

RMSE, normalized by the mean absolute label

forecast_type: str = 'mean'

static normalize_root_mean_squared_error(root_mean_squared_error: numpy.ndarray, mean_absolute_label: numpy.ndarray) → numpy.ndarray

class gluonts.ev.metrics.OWA(forecast_type: str = '0.5')

Bases: object

Overall Weighted Average

static calculate_OWA(smape: numpy.ndarray, smape_naive2: numpy.ndarray, mase: numpy.ndarray, mase_naive2: numpy.ndarray) → numpy.ndarray

forecast_type: str = '0.5'

class gluonts.ev.metrics.RMSE(forecast_type: str = 'mean')

Bases: object

Root Mean Squared Error

forecast_type: str = 'mean'

static root_mean_squared_error(mean_squared_error: numpy.ndarray) → numpy.ndarray

class gluonts.ev.metrics.SMAPE(forecast_type: str = '0.5')

Bases: object

Symmetric Mean Absolute Percentage Error

forecast_type: str = '0.5'

class gluonts.ev.metrics.SumAbsoluteError(forecast_type: str = '0.5')

Bases: object

forecast_type: str = '0.5'

class gluonts.ev.metrics.SumError(forecast_type: str = '0.5')

Bases: object

forecast_type: str = '0.5'

class gluonts.ev.metrics.SumQuantileLoss(q: float)

Bases: object

q: float

class gluonts.ev.metrics.WeightedSumQuantileLoss(q: float)

Bases: object

q: float

static weight_sum_quantile_loss(sum_quantile_loss: numpy.ndarray, sum_absolute_label: numpy.ndarray) → numpy.ndarray

gluonts.ev.metrics.mean_absolute_label(axis: Optional[int] = None) → gluonts.ev.evaluator.DirectEvaluator

gluonts.ev.metrics.sum_absolute_label(axis: Optional[int] = None) → gluonts.ev.evaluator.DirectEvaluator