gluonts.model.trivial.mean module

class gluonts.model.trivial.mean.MeanEstimator(prediction_length: pydantic.types.PositiveInt, freq: str, num_samples: pydantic.types.PositiveInt)

Bases: gluonts.model.estimator.Estimator

An Estimator that computes the mean targets in the training data, in the trailing prediction_length observations, and produces a ConstantPredictor that always predicts such mean value.

Parameters

• prediction_length – Prediction horizon.

• freq – Frequency of the predicted data.

• num_samples – Number of samples to include in the forecasts. Not that the samples produced by this predictor will all be identical.

freq = None

lead_time = None

prediction_length = None

train(training_data: Iterable[Dict[str, Any]], validation_dataset: Optional[Iterable[Dict[str, Any]]] = None) → gluonts.model.trivial.constant.ConstantPredictor

Train the estimator on the given data.

Parameters

• training_data – Dataset to train the model on.

• validation_data – Dataset to validate the model on during training.

Returns

The predictor containing the trained model.

Return type

Predictor

class gluonts.model.trivial.mean.MeanPredictor(prediction_length: int, freq: str, num_samples: int = 100, context_length: Optional[int] = None)

Bases: gluonts.model.predictor.RepresentablePredictor, gluonts.model.predictor.FallbackPredictor

A Predictor that predicts the samples based on the mean of the last context_length elements of the input target.

Parameters

• context_length – Length of the target context used to condition the predictions.

• prediction_length – Length of the prediction horizon.

• num_samples – Number of samples to use to construct SampleForecast objects for every prediction.

• freq – Frequency of the predicted data.

predict_item(item: Dict[str, Any]) → gluonts.model.forecast.SampleForecast

class gluonts.model.trivial.mean.MovingAveragePredictor(prediction_length: int, freq: str, context_length: Optional[int] = None)

Bases: gluonts.model.predictor.RepresentablePredictor

A Predictor that predicts the moving average based on the last context_length elements of the input target.

If prediction_length = 1, the output is the moving average based on the last context_length elements of the input target.

If prediction_length > 1, the output is the moving average based on the last context_length elements of the input target, where previously calculated moving averages are appended at the end of the input target. Hence, for prediction_length larger than context_length, there will be cases where the moving average is calculated on top of previous moving averages.

Parameters

• context_length – Length of the target context used to condition the predictions.

• prediction_length – Length of the prediction horizon.

• freq – Frequency of the predicted data.

predict_item(item: Dict[str, Any]) → gluonts.model.forecast.SampleForecast