m4_freq="Yearly",
pandas_freq="12M",
prediction_length=6,
),
"m4_yearly_other": partial(
generate_m4_dataset,
m4_freq="Yearly",
pandas_freq="12M",
prediction_length=6,
),
}
)
dataset_names = list(dataset_recipes.keys())
default_dataset_path = get_download_path() / "datasets"
def materialize_dataset(
dataset_name: str,
path: Path = default_dataset_path,
regenerate: bool = False,
) -> Path:
"""
Ensures that the dataset is materialized under the `path / dataset_name`
path.
Parameters
----------
dataset_name
name of the dataset, for instance "m4_hourly"
if __name__ == "__main__":
dataset = get_dataset("exchange_rate")
estimator = SimpleFeedForwardEstimator(
prediction_length=dataset.metadata.prediction_length,
freq=dataset.metadata.freq,
trainer=Trainer(epochs=5, num_batches_per_epoch=10),
)
predictor = estimator.train(dataset.train)
# save the trained model in a path ~/.mxnet/gluon-ts/feedforward/
# or $MXNET_HOME/feedforward if MXNET_HOME is defined
model_path = get_download_path() / "feedforward"
os.makedirs(model_path, exist_ok=True)
predictor.serialize(model_path)
# loads it back and evaluate predictions accuracy with the deserialized model
predictor_deserialized = Predictor.deserialize(model_path)
forecast_it, ts_it = make_evaluation_predictions(
dataset.test, predictor=predictor_deserialized, num_samples=100)
agg_metrics, item_metrics = Evaluator()(ts_it,
forecast_it,
num_series=len(dataset.test))
pprint.pprint(agg_metrics)
