Source code for gluonts.nursery.autogluon_tabular.example

# Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License").
# You may not use this file except in compliance with the License.
# A copy of the License is located at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# or in the "license" file accompanying this file. This file is distributed
# on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either
# express or implied. See the License for the specific language governing
# permissions and limitations under the License.


from itertools import islice
from pathlib import Path
import matplotlib.pyplot as plt
import os


from gluonts.dataset.util import to_pandas
from gluonts.dataset.repository.datasets import get_dataset
from gluonts.nursery.autogluon_tabular import TabularEstimator
from gluonts.model.predictor import Predictor


[docs]def run_example(): dataset = get_dataset("electricity") serialize_path = Path("GluonTSTabularPredictor") estimator = TabularEstimator( freq="H", prediction_length=24, time_limit=10, # two minutes for training disable_auto_regression=True, # makes prediction faster, but potentially less accurate ) n_train = 5 training_data = list(islice(dataset.train, n_train)) predictor = estimator.train( training_data=training_data, ) os.makedirs(serialize_path, exist_ok=True) predictor.serialize(serialize_path) predictor = None predictor = Predictor.deserialize(serialize_path) forecasts = list(predictor.predict(training_data)) for entry, forecast in zip(training_data, forecasts): ts = to_pandas(entry) plt.figure() plt.plot(ts[-7 * predictor.prediction_length :], label="target") forecast.plot() plt.show()
if __name__ == "__main__": run_example()