gluonts.model.forecast module

class gluonts.model.forecast.Config

Bases: pydantic.main.BaseModel

class Config

Bases: object

allow_population_by_field_name = True

extra = 'allow'

num_samples: int = None

output_types: Set[OutputType] = None

quantiles: List[str] = None

class gluonts.model.forecast.Forecast

Bases: object

A abstract class representing predictions.

as_json_dict(config: gluonts.model.forecast.Config) → dict

copy_aggregate(agg_fun: Callable)

Returns a new Forecast object with a time series aggregated over the dimension axis.

Parameters

agg_fun – Aggregation function that defines the aggregation operation (typically mean or sum).

copy_dim(dim: int)

Returns a new Forecast object with only the selected sub-dimension.

Parameters

dim – The returned forecast object will only represent this dimension.

dim() → int

Returns the dimensionality of the forecast object.

freq: str = None

property index

info: Optional[Dict] = None

item_id: Optional[str] = None

mean: np.ndarray = None

property median

plot(prediction_intervals=(50.0, 90.0), show_mean=False, color='b', label=None, output_file=None, *args, **kwargs)

Plots the median of the forecast as well as confidence bounds. (requires matplotlib and pandas).

Parameters

• prediction_intervals (float or list of floats in [0, 100]) – Confidence interval size(s). If a list, it will stack the error plots for each confidence interval. Only relevant for error styles with “ci” in the name.

• show_mean (boolean) – Whether to also show the mean of the forecast.

• color (matplotlib color name or dictionary) – The color used for plotting the forecast.

• label (string) – A label (prefix) that is used for the forecast

• output_file (str or None, default None) – Output path for the plot file. If None, plot is not saved to file.

• args – Other arguments are passed to main plot() call

• kwargs – Other keyword arguments are passed to main plot() call

prediction_length: int = None

quantile(q: Union[float, str]) → numpy.ndarray

Computes a quantile from the predicted distribution.

Parameters

q – Quantile to compute.

Returns

Value of the quantile across the prediction range.

Return type

numpy.ndarray

quantile_ts(q: Union[float, str]) → pandas.core.series.Series

start_date: pd.Timestamp = None

class gluonts.model.forecast.OutputType

Bases: str, enum.Enum

An enumeration.

mean = 'mean'

quantiles = 'quantiles'

samples = 'samples'

class gluonts.model.forecast.Quantile(value, name)

Bases: tuple

classmethod checked(value: float, name: str) → gluonts.model.forecast.Quantile

property coverage_name

classmethod from_float(quantile: float) → gluonts.model.forecast.Quantile

classmethod from_str(quantile: str) → gluonts.model.forecast.Quantile

property loss_name

property name

Alias for field number 1

classmethod parse(quantile: Union[Quantile, float, str]) → gluonts.model.forecast.Quantile

Produces equivalent float and string representation of a given quantile level.

>>> Quantile.parse(0.1)
Quantile(value=0.1, name='0.1')

>>> Quantile.parse('0.2')
Quantile(value=0.2, name='0.2')

>>> Quantile.parse('0.20')
Quantile(value=0.2, name='0.20')

>>> Quantile.parse('p99')
Quantile(value=0.99, name='0.99')

Parameters

quantile – Quantile, can be a float a str representing a float e.g. ‘0.1’ or a quantile string of the form ‘p0.1’.

Returns

A tuple containing both a float and a string representation of the input quantile level.

Return type

Quantile

property value

Alias for field number 0

property weighted_loss_name

class gluonts.model.forecast.QuantileForecast(forecast_arrays: numpy.ndarray, start_date: pandas._libs.tslibs.timestamps.Timestamp, freq: str, forecast_keys: List[str], item_id: Optional[str] = None, info: Optional[Dict] = None)

Bases: gluonts.model.forecast.Forecast

A Forecast that contains arrays (i.e. time series) for quantiles and mean

Parameters

• forecast_arrays – An array of forecasts

• start_date – start of the forecast

• freq – forecast frequency

• forecast_keys – A list of quantiles of the form ‘0.1’, ‘0.9’, etc., and potentially ‘mean’. Each entry corresponds to one array in forecast_arrays.

• info – additional information that the forecaster may provide e.g. estimated parameters, number of iterations ran etc.

dim() → int

Returns the dimensionality of the forecast object.

freq = None

info = None

item_id = None

property mean

Forecast mean.

plot(label=None, output_file=None, keys=None, *args, **kwargs)

Plots the median of the forecast as well as confidence bounds. (requires matplotlib and pandas).

Parameters

• prediction_intervals (float or list of floats in [0, 100]) – Confidence interval size(s). If a list, it will stack the error plots for each confidence interval. Only relevant for error styles with “ci” in the name.

• show_mean (boolean) – Whether to also show the mean of the forecast.

• color (matplotlib color name or dictionary) – The color used for plotting the forecast.

• label (string) – A label (prefix) that is used for the forecast

• output_file (str or None, default None) – Output path for the plot file. If None, plot is not saved to file.

• args – Other arguments are passed to main plot() call

• kwargs – Other keyword arguments are passed to main plot() call

prediction_length = None

quantile(q: Union[float, str]) → numpy.ndarray

Computes a quantile from the predicted distribution.

Parameters

q – Quantile to compute.

Returns

Value of the quantile across the prediction range.

Return type

numpy.ndarray

start_date = None

class gluonts.model.forecast.SampleForecast(samples: numpy.ndarray, start_date: pandas._libs.tslibs.timestamps.Timestamp, freq: str, item_id: Optional[str] = None, info: Optional[Dict] = None)

Bases: gluonts.model.forecast.Forecast

A Forecast object, where the predicted distribution is represented internally as samples.

Parameters

• samples – Array of size (num_samples, prediction_length) (1D case) or (num_samples, prediction_length, target_dim) (multivariate case)

• start_date – start of the forecast

• freq – forecast frequency

• info – additional information that the forecaster may provide e.g. estimated parameters, number of iterations ran etc.

as_json_dict(config: gluonts.model.forecast.Config) → dict

copy_aggregate(agg_fun: Callable) → gluonts.model.forecast.SampleForecast

Returns a new Forecast object with a time series aggregated over the dimension axis.

Parameters

agg_fun – Aggregation function that defines the aggregation operation (typically mean or sum).

copy_dim(dim: int) → gluonts.model.forecast.SampleForecast

Returns a new Forecast object with only the selected sub-dimension.

Parameters

dim – The returned forecast object will only represent this dimension.

dim() → int

Returns the dimensionality of the forecast object.

freq = None

info = None

item_id = None

property mean

Forecast mean.

property mean_ts

Forecast mean, as a pandas.Series object.

property num_samples

The number of samples representing the forecast.

property prediction_length

Time length of the forecast.

quantile(q: Union[float, str]) → numpy.ndarray

Computes a quantile from the predicted distribution.

Parameters

q – Quantile to compute.

Returns

Value of the quantile across the prediction range.

Return type

numpy.ndarray

start_date = None

to_quantile_forecast(quantiles: List[Union[str, float]]) → gluonts.model.forecast.QuantileForecast