gluonts.model.forecast module#

class gluonts.model.forecast.Config(*, num_eval_samples: int = 100, output_types: Set[gluonts.model.forecast.OutputType] = {OutputType.mean, OutputType.quantiles}, quantiles: List[str] = ['0.1', '0.5', '0.9'], **extra_data: Any)[source]#

Bases: pydantic.main.BaseModel

class Config[source]#

Bases: object

allow_population_by_field_name = True#

extra = 'allow'#

num_samples: int#

output_types: Set[gluonts.model.forecast.OutputType]#

quantiles: List[str]#

class gluonts.model.forecast.ExponentialTailApproximation(x_coord: List[float], y_coord: List[numpy.ndarray], tol: float = 1e-08)[source]#

Bases: object

Approximate function on tails based on knots and make a inference on query point. Can be used for either interpolation or extrapolation on tails.

Parameters

x_coord – x-coordinates of the data points must be in increasing order.
y_coord – y-coordinates of the data points - may be a higher numpy array.
tol – tolerance when performing the division and computing the log in the exponential extrapolation.

init_exponential_tail_weights() → Tuple[float, float][source]#

Initialize the weight of exponentially decaying tail functions based on two extreme points on the left and right, respectively.

Returns: beta coefficient for left and right tails.
Return type: Tuple

left(x: float) → numpy.ndarray[source]#

Return the inference made on exponentially decaying tail functions.

For left tail, x = exp(beta * (q - alpha)) For right tail, x = 1 - exp(-beta * (q - alpha))

E.g. for x = self.x_coord[0] or self.x_coord[1], return value is exactly self.y_coord[0] or self.y_coord[1], respectively.

Parameters: x – x-coordinate to evaluate the right tail.

right(x: float) → numpy.ndarray[source]#

Return the inference made on exponentially decaying tail functions.

For left tail, x = exp(beta * (q - alpha)) For right tail, x = 1 - exp(-beta * (q - alpha))

E.g. for x = self.x_coord[-1] or self.x_coord[-2] , return value is exactly self.y_coord[-1] or self.y_coord[-2] respectively. :param x: x-coordinate to evaluate the right tail.

tail_range(default_left_tail=0.1, default_right_tail=0.9)[source]#: Return an effective range of left and right tails.

class gluonts.model.forecast.Forecast[source]#

Bases: object

A abstract class representing predictions.

as_json_dict(config: gluonts.model.forecast.Config) → dict[source]#

copy_aggregate(agg_fun: Callable)[source]#

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)[source]#

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

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

dim() → int[source]#: Returns the dimensionality of the forecast object.

property freq#

property index: pandas.core.indexes.period.PeriodIndex#

info: Optional[Dict]#

item_id: Optional[str]#

mean: numpy.ndarray#

property median: numpy.ndarray#

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

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#

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

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[source]#

start_date: pandas._libs.tslibs.period.Period#

class gluonts.model.forecast.LinearInterpolation(x_coord: List[float], y_coord: List[numpy.ndarray], tol: float = 1e-08)[source]#

Bases: object

Linear interpolation based on datapoints (x_coord, y_coord)

Parameters

x_coord – x-coordinates of the data points must be in increasing order.
y_coord – y-coordinates of the data points - may be a list of lists.
tol – tolerance when performing the division in the linear interpolation.

linear_interpolation(x: float) → numpy.ndarray[source]#

If x is out of interpolation range, return smallest or largest value. Otherwise, find two nearest points [x_1, y_1], [x_2, y_2] and return its linear interpolation.

y = (x_2 - x)/(x_2 - x_1) * y_1 + (x - x_1)/(x_2 - x_1) * y_2.

Parameters: x – x-coordinate to evaluate the interpolated points.
Returns: Interpolated values same shape as self.y_coord
Return type: np.ndarray

class gluonts.model.forecast.OutputType(value)[source]#

Bases: str, enum.Enum

An enumeration.

mean = 'mean'#

quantiles = 'quantiles'#

samples = 'samples'#

class gluonts.model.forecast.Quantile(*, value: float, name: str)[source]#

Bases: pydantic.main.BaseModel

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

property coverage_name#

classmethod from_float(quantile: float) → gluonts.model.forecast.Quantile[source]#

classmethod from_str(quantile: str) → gluonts.model.forecast.Quantile[source]#

property loss_name#

name: str#

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

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

value: float#

property weighted_loss_name#

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

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 (pandas._libs.tslibs.period.Period) – start of the forecast
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 (Optional[Dict]) – additional information that the forecaster may provide e.g. estimated parameters, number of iterations ran etc.

dim() → int[source]#: Returns the dimensionality of the forecast object.

info: Optional[Dict]#

item_id: Optional[str]#

property mean: numpy.ndarray#: Forecast mean.

plot(label=None, output_file=None, keys=None, *args, **kwargs)[source]#

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#

quantile(inference_quantile: Union[float, str]) → numpy.ndarray[source]#

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: pandas._libs.tslibs.period.Period#

class gluonts.model.forecast.SampleForecast(samples: numpy.ndarray, start_date: pandas._libs.tslibs.period.Period, item_id: Optional[str] = None, info: Optional[Dict] = None)[source]#

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 (pandas._libs.tslibs.period.Period) – start of the forecast
info (Optional[Dict]) – 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[source]#

copy_aggregate(agg_fun: Callable) → gluonts.model.forecast.SampleForecast[source]#

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[source]#

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

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

dim() → int[source]#: Returns the dimensionality of the forecast object.

info: Optional[Dict]#

item_id: Optional[str]#

property mean: numpy.ndarray#: Forecast mean.

property mean_ts: pandas.core.series.Series#: 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[source]#

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: pandas._libs.tslibs.period.Period#

to_quantile_forecast(quantiles: List[str]) → gluonts.model.forecast.QuantileForecast[source]#