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.. _sphx_glr_examples_20_basics_example_time_series_forecasting.py:


======================
Time Series Forecasting
======================

The following example shows how to fit a sample forecasting model
with AutoPyTorch. This is only a dummmy example because of the limited size of the dataset.
Thus, it could be possible that the AutoPyTorch model does not perform as well as a dummy predictor

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.. code-block:: default

    import os
    import tempfile as tmp
    import warnings
    import copy

    os.environ['JOBLIB_TEMP_FOLDER'] = tmp.gettempdir()
    os.environ['OMP_NUM_THREADS'] = '1'
    os.environ['OPENBLAS_NUM_THREADS'] = '1'
    os.environ['MKL_NUM_THREADS'] = '1'

    warnings.simplefilter(action='ignore', category=UserWarning)
    warnings.simplefilter(action='ignore', category=FutureWarning)

    from sktime.datasets import load_longley
    targets, features = load_longley()

    forecasting_horizon = 3

    # Dataset optimized by APT-TS can be a list of np.ndarray/ pd.DataFrame where each series represents an element in the
    # list, or a single pd.DataFrame that records the series
    # index information: to which series the timestep belongs? This id can be stored as the DataFrame's index or a separate
    # column
    # Within each series, we take the last forecasting_horizon as test targets. The items before that as training targets
    # Normally the value to be forecasted should follow the training sets
    y_train = [targets[: -forecasting_horizon]]
    y_test = [targets[-forecasting_horizon:]]

    # same for features. For uni-variant models, X_train, X_test can be omitted and set as None
    X_train = [features[: -forecasting_horizon]]
    # Here x_test indicates the 'known future features': they are the features known previously, features that are unknown
    # could be replaced with NAN or zeros (which will not be used by our networks). If no feature is known beforehand,
    # we could also omit X_test
    known_future_features = list(features.columns)
    X_test = [features[-forecasting_horizon:]]

    start_times = [targets.index.to_timestamp()[0]]
    freq = '1Y'

    from autoPyTorch.api.time_series_forecasting import TimeSeriesForecastingTask







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Build and fit a forecaster
==========================

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.. code-block:: default

    api = TimeSeriesForecastingTask()








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Search for an ensemble of machine learning algorithms
=====================================================

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.. code-block:: default

    api.search(
        X_train=X_train,
        y_train=copy.deepcopy(y_train),
        X_test=X_test,
        optimize_metric='mean_MASE_forecasting',
        n_prediction_steps=forecasting_horizon,
        memory_limit=16 * 1024,   # Currently, forecasting models use much more memories
        freq=freq,
        start_times=start_times,
        func_eval_time_limit_secs=50,
        total_walltime_limit=60,
        min_num_test_instances=1000,  # proxy validation sets. This only works for the tasks with more than 1000 series
        known_future_features=known_future_features,
    )


    from autoPyTorch.datasets.time_series_dataset import TimeSeriesSequence

    test_sets = []

    # We could construct test sets from scratch
    for feature, future_feature, target, start_time in zip(X_train, X_test,y_train, start_times):
        test_sets.append(
            TimeSeriesSequence(X=feature.values,
                               Y=target.values,
                               X_test=future_feature.values,
                               start_time=start_time,
                               is_test_set=True,
                               # additional information required to construct a new time series sequence
                               **api.dataset.sequences_builder_kwargs
                               )
        )
    # Alternatively, if we only want to forecast the value after the X_train, we could directly ask datamanager to
    # generate a test set:
    # test_sets2 = api.dataset.generate_test_seqs()

    pred = api.predict(test_sets)








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