flexmeasures.data.models.forecasting
Modules
Classes
- class flexmeasures.data.models.forecasting.Forecaster(config: dict | None = None, save_config=True, save_parameters=False, **kwargs)
- _clean_parameters(parameters: dict) dict
Clean out DataGenerator parameters that should not be stored as DataSource attributes.
These parameters are already contained in the TimedBelief:
end-date: as the event end
max-forecast-horizon: as the maximum belief horizon of the beliefs for a given event
forecast-frequency: as the spacing between unique belief times
probabilistic: as the cumulative_probability of each belief
sensor-to-save: as the sensor on which the beliefs are recorded
Other:
model-save-dir: used internally for the train and predict pipelines to save and load the model
output-path: for exporting forecasts to file, more of a developer feature
as-job: only indicates whether the computation was offloaded to a worker
- _compute(check_output_resolution=True, as_job: bool = False, **kwargs) list[dict[str, Any]]
This method triggers the creation of a new forecast.
The same object can generate multiple forecasts with different start, end, resolution and belief_time values.
- Parameters:
check_output_resolution – If True, checks each output for whether the event_resolution matches that of the sensor it is supposed to be recorded on.
as_job – If True, runs as a job.
- class flexmeasures.data.models.forecasting.SuppressTorchWarning(name='')
Suppress specific Torch warnings from Darts library about model availability.
- filter(record)
Determine if the specified record is to be logged.
Returns True if the record should be logged, or False otherwise. If deemed appropriate, the record may be modified in-place.