gluonts.ev.metrics module

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

Bases: BaseMetricDefinition

static mean(**quantile_losses: ndarray) → ndarray

quantile_levels: Collection[float]

class gluonts.ev.metrics.BaseMetricDefinition

Bases: object

add(*others)

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

Bases: BaseMetricDefinition

q: float

class gluonts.ev.metrics.DerivedMetric(name: str, metrics: Dict[str, Metric], post_process: Callable)

Bases: Metric

A Metric that is computed using other metrics.

A derived metric updates multiple, simpler metrics independently and in the end combines their results as defined in post_process.

get() → ndarray

metrics: Dict[str, Metric]

post_process: Callable

update(data: Mapping[str, ndarray]) → Self

Update metric using a single data instance.

class gluonts.ev.metrics.DirectMetric(name: str, stat: Callable, aggregate: Aggregation)

Bases: Metric

A Metric which uses a single function and aggregation strategy.

aggregate: Aggregation

get() → ndarray

stat: Callable

update(data: Mapping[str, ndarray]) → Self

Update metric using a single data instance.

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

Bases: BaseMetricDefinition

Mean Absolute Error.

forecast_type: str = '0.5'

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

Bases: BaseMetricDefinition

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

quantile_levels: Collection[float]

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

Bases: BaseMetricDefinition

Mean Absolute Percentage Error.

forecast_type: str = '0.5'

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

Bases: BaseMetricDefinition

Mean Absolute Scaled Error.

forecast_type: str = '0.5'

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

Bases: BaseMetricDefinition

Mean Squared Error.

forecast_type: str = 'mean'

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

Bases: BaseMetricDefinition

Mean Scaled Interval Score.

alpha: float = 0.05

class gluonts.ev.metrics.MeanAbsoluteLabel

Bases: BaseMetricDefinition

class gluonts.ev.metrics.MeanScaledQuantileLoss(q: 'float')

Bases: BaseMetricDefinition

q: float

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

Bases: BaseMetricDefinition

static mean(**quantile_losses: ndarray) → ndarray

quantile_levels: Collection[float]

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

Bases: BaseMetricDefinition

static mean(**quantile_losses: ndarray) → ndarray

quantile_levels: Collection[float]

class gluonts.ev.metrics.Metric(name: 'str')

Bases: object

get() → ndarray

name: str

update(data: Mapping[str, ndarray]) → Self

Update metric using a single data instance.

update_all(stream: Iterator[Mapping[str, ndarray]]) → Self

Update metric using a stream of data instances.

class gluonts.ev.metrics.MetricCollection(metrics: 'List[Metric]')

Bases: object

get() → Dict[str, ndarray]

metrics: List[Metric]

update(data: Mapping[str, ndarray]) → Self

Update metrics using a single data instance.

update_all(stream: Iterator[Mapping[str, ndarray]]) → Self

Update metrics using a stream of data instances.

class gluonts.ev.metrics.MetricDefinition(*args, **kwargs)

Bases: Protocol

class gluonts.ev.metrics.MetricDefinitionCollection(metrics: 'List[BaseMetricDefinition]')

Bases: BaseMetricDefinition

metrics: List[BaseMetricDefinition]

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

Bases: BaseMetricDefinition

Normalized Deviation.

forecast_type: str = '0.5'

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

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

Bases: BaseMetricDefinition

RMSE, normalized by the mean absolute label.

forecast_type: str = 'mean'

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

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

Bases: BaseMetricDefinition

Overall Weighted Average.

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

forecast_type: str = '0.5'

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

Bases: BaseMetricDefinition

Root Mean Squared Error.

forecast_type: str = 'mean'

static root_mean_squared_error(mean_squared_error: ndarray) → ndarray

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

Bases: BaseMetricDefinition

Symmetric Mean Absolute Percentage Error.

forecast_type: str = '0.5'

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

Bases: BaseMetricDefinition

forecast_type: str = '0.5'

class gluonts.ev.metrics.SumAbsoluteLabel

Bases: BaseMetricDefinition

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

Bases: BaseMetricDefinition

forecast_type: str = '0.5'

class gluonts.ev.metrics.SumNumMaskedTargetValues

Bases: BaseMetricDefinition

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

Bases: BaseMetricDefinition

q: float

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

Bases: BaseMetricDefinition

q: float

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