gluonts.model.forecast module¶
-
class
gluonts.model.forecast.
Config
[source]¶ Bases:
pydantic.main.BaseModel
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num_samples
: int = None¶
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output_types
: Set[OutputType] = None¶
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quantiles
: List[str] = None¶
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class
gluonts.model.forecast.
Forecast
[source]¶ Bases:
object
A abstract class representing predictions.
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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).
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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.
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freq
: str = None¶
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property
index
¶
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info
: Optional[Dict] = None¶
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item_id
: Optional[str] = None¶
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mean
: np.ndarray = None¶
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property
median
¶
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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
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prediction_length
: int = None¶
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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
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start_date
: pd.Timestamp = None¶
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class
gluonts.model.forecast.
OutputType
[source]¶ Bases:
str
,enum.Enum
An enumeration.
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mean
= 'mean'¶
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quantiles
= 'quantiles'¶
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samples
= 'samples'¶
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class
gluonts.model.forecast.
Quantile
(value, name)[source]¶ Bases:
tuple
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property
coverage_name
¶
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property
loss_name
¶
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property
name
¶ Alias for field number 1
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classmethod
parse
(quantile: Union[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
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property
value
¶ Alias for field number 0
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property
weighted_loss_name
¶
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property
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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)[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 – 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.
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freq
= None¶
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info
= None¶
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item_id
= None¶
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property
mean
¶ Forecast mean.
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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
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prediction_length
= None¶
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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
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start_date
= None¶
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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)[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 – start of the forecast
freq – forecast frequency
info – additional information that the forecaster may provide e.g. estimated parameters, number of iterations ran etc.
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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.
-
freq
= None¶
-
info
= None¶
-
item_id
= None¶
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property
mean
¶ Forecast mean.
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property
mean_ts
¶ Forecast mean, as a pandas.Series object.
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property
num_samples
¶ The number of samples representing the forecast.
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property
prediction_length
¶ Time length of the forecast.
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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
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start_date
= None¶