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How to use the gluonts.dataset.field_names.FieldName.FEAT_TIME function in gluonts

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github awslabs / gluon-ts / src / gluonts / model / seq2seq / _seq2seq_estimator.py View on Github external
def create_transformation(self) -> transform.Transformation:
        return transform.Chain(
            trans=[
                transform.AsNumpyArray(
                    field=FieldName.TARGET, expected_ndim=1
                ),
                transform.AddTimeFeatures(
                    start_field=FieldName.START,
                    target_field=FieldName.TARGET,
                    output_field=FieldName.FEAT_TIME,
                    time_features=time_features_from_frequency_str(self.freq),
                    pred_length=self.prediction_length,
                ),
                transform.VstackFeatures(
                    output_field=FieldName.FEAT_DYNAMIC_REAL,
                    input_fields=[FieldName.FEAT_TIME],
                ),
                transform.SetFieldIfNotPresent(
                    field=FieldName.FEAT_STATIC_CAT, value=[0.0]
                ),
                transform.AsNumpyArray(
                    field=FieldName.FEAT_STATIC_CAT, expected_ndim=1
                ),
                transform.InstanceSplitter(
                    target_field=FieldName.TARGET,
                    is_pad_field=FieldName.IS_PAD,
                    start_field=FieldName.START,
                    forecast_start_field=FieldName.FORECAST_START,
                    train_sampler=ExpectedNumInstanceSampler(num_instances=1),
                    past_length=self.context_length,
                    future_length=self.prediction_length,
                    time_series_fields=[FieldName.FEAT_DYNAMIC_REAL],
github awslabs / gluon-ts / src / gluonts / model / wavenet / _estimator.py View on Github external
),
                AddTimeFeatures(
                    start_field=FieldName.START,
                    target_field=FieldName.TARGET,
                    output_field=FieldName.FEAT_TIME,
                    time_features=time_features_from_frequency_str(self.freq),
                    pred_length=self.prediction_length,
                ),
                AddAgeFeature(
                    target_field=FieldName.TARGET,
                    output_field=FieldName.FEAT_AGE,
                    pred_length=self.prediction_length,
                ),
                VstackFeatures(
                    output_field=FieldName.FEAT_TIME,
                    input_fields=[FieldName.FEAT_TIME, FieldName.FEAT_AGE],
                ),
                SetFieldIfNotPresent(
                    field=FieldName.FEAT_STATIC_CAT, value=[0.0]
                ),
                AsNumpyArray(field=FieldName.FEAT_STATIC_CAT, expected_ndim=1),
                InstanceSplitter(
                    target_field=FieldName.TARGET,
                    is_pad_field=FieldName.IS_PAD,
                    start_field=FieldName.START,
                    forecast_start_field=FieldName.FORECAST_START,
                    train_sampler=ExpectedNumInstanceSampler(num_instances=1),
                    past_length=self.context_length,
                    future_length=pred_length,
                    output_NTC=False,
                    time_series_fields=[
                        FieldName.FEAT_TIME,
github awslabs / gluon-ts / src / gluonts / model / transformer / _estimator.py View on Github external
)
            + [
                AsNumpyArray(field=FieldName.FEAT_STATIC_CAT, expected_ndim=1),
                AsNumpyArray(
                    field=FieldName.TARGET,
                    # in the following line, we add 1 for the time dimension
                    expected_ndim=1 + len(self.distr_output.event_shape),
                ),
                AddObservedValuesIndicator(
                    target_field=FieldName.TARGET,
                    output_field=FieldName.OBSERVED_VALUES,
                ),
                AddTimeFeatures(
                    start_field=FieldName.START,
                    target_field=FieldName.TARGET,
                    output_field=FieldName.FEAT_TIME,
                    time_features=self.time_features,
                    pred_length=self.prediction_length,
                ),
                AddAgeFeature(
                    target_field=FieldName.TARGET,
                    output_field=FieldName.FEAT_AGE,
                    pred_length=self.prediction_length,
                    log_scale=True,
                ),
                VstackFeatures(
                    output_field=FieldName.FEAT_TIME,
                    input_fields=[FieldName.FEAT_TIME, FieldName.FEAT_AGE]
                    + (
                        [FieldName.FEAT_DYNAMIC_REAL]
                        if self.use_feat_dynamic_real
                        else []
github awslabs / gluon-ts / src / gluonts / model / gp_forecaster / _estimator.py View on Github external
def create_transformation(self) -> Transformation:
        return Chain(
            [
                AsNumpyArray(field=FieldName.TARGET, expected_ndim=1),
                AddTimeFeatures(
                    start_field=FieldName.START,
                    target_field=FieldName.TARGET,
                    output_field=FieldName.FEAT_TIME,
                    time_features=self.time_features,
                    pred_length=self.prediction_length,
                ),
                SetFieldIfNotPresent(
                    field=FieldName.FEAT_STATIC_CAT, value=[0.0]
                ),
                AsNumpyArray(field=FieldName.FEAT_STATIC_CAT, expected_ndim=1),
                CanonicalInstanceSplitter(
                    target_field=FieldName.TARGET,
                    is_pad_field=FieldName.IS_PAD,
                    start_field=FieldName.START,
                    forecast_start_field=FieldName.FORECAST_START,
                    instance_sampler=TestSplitSampler(),
                    time_series_fields=[FieldName.FEAT_TIME],
                    instance_length=self.context_length,
                    use_prediction_features=True,
github awslabs / gluon-ts / src / gluonts / model / deepar / _estimator.py View on Github external
start_field=FieldName.START,
                    target_field=FieldName.TARGET,
                    output_field=FieldName.FEAT_TIME,
                    time_features=self.time_features,
                    pred_length=self.prediction_length,
                ),
                AddAgeFeature(
                    target_field=FieldName.TARGET,
                    output_field=FieldName.FEAT_AGE,
                    pred_length=self.prediction_length,
                    log_scale=True,
                    dtype=self.dtype,
                ),
                VstackFeatures(
                    output_field=FieldName.FEAT_TIME,
                    input_fields=[FieldName.FEAT_TIME, FieldName.FEAT_AGE]
                    + (
                        [FieldName.FEAT_DYNAMIC_REAL]
                        if self.use_feat_dynamic_real
                        else []
                    ),
                ),
                InstanceSplitter(
                    target_field=FieldName.TARGET,
                    is_pad_field=FieldName.IS_PAD,
                    start_field=FieldName.START,
                    forecast_start_field=FieldName.FORECAST_START,
                    train_sampler=ExpectedNumInstanceSampler(num_instances=1),
                    past_length=self.history_length,
                    future_length=self.prediction_length,
                    time_series_fields=[
                        FieldName.FEAT_TIME,
github awslabs / gluon-ts / src / gluonts / model / wavenet / _estimator.py View on Github external
def create_transformation(
        self, bin_edges: np.ndarray, pred_length: int
    ) -> transform.Transformation:
        return Chain(
            [
                AsNumpyArray(field=FieldName.TARGET, expected_ndim=1),
                AddObservedValuesIndicator(
                    target_field=FieldName.TARGET,
                    output_field=FieldName.OBSERVED_VALUES,
                ),
                AddTimeFeatures(
                    start_field=FieldName.START,
                    target_field=FieldName.TARGET,
                    output_field=FieldName.FEAT_TIME,
                    time_features=time_features_from_frequency_str(self.freq),
                    pred_length=self.prediction_length,
                ),
                AddAgeFeature(
                    target_field=FieldName.TARGET,
                    output_field=FieldName.FEAT_AGE,
                    pred_length=self.prediction_length,
                ),
                VstackFeatures(
                    output_field=FieldName.FEAT_TIME,
                    input_fields=[FieldName.FEAT_TIME, FieldName.FEAT_AGE],
                ),
                SetFieldIfNotPresent(
                    field=FieldName.FEAT_STATIC_CAT, value=[0.0]
                ),
                AsNumpyArray(field=FieldName.FEAT_STATIC_CAT, expected_ndim=1),
github awslabs / gluon-ts / src / gluonts / model / deepar / _estimator.py View on Github external
),
                AsNumpyArray(
                    field=FieldName.TARGET,
                    # in the following line, we add 1 for the time dimension
                    expected_ndim=1 + len(self.distr_output.event_shape),
                    dtype=self.dtype,
                ),
                AddObservedValuesIndicator(
                    target_field=FieldName.TARGET,
                    output_field=FieldName.OBSERVED_VALUES,
                    dtype=self.dtype,
                ),
                AddTimeFeatures(
                    start_field=FieldName.START,
                    target_field=FieldName.TARGET,
                    output_field=FieldName.FEAT_TIME,
                    time_features=self.time_features,
                    pred_length=self.prediction_length,
                ),
                AddAgeFeature(
                    target_field=FieldName.TARGET,
                    output_field=FieldName.FEAT_AGE,
                    pred_length=self.prediction_length,
                    log_scale=True,
                    dtype=self.dtype,
                ),
                VstackFeatures(
                    output_field=FieldName.FEAT_TIME,
                    input_fields=[FieldName.FEAT_TIME, FieldName.FEAT_AGE]
                    + (
                        [FieldName.FEAT_DYNAMIC_REAL]
                        if self.use_feat_dynamic_real
github awslabs / gluon-ts / src / gluonts / model / deepstate / _estimator.py View on Github external
),
                AddTimeFeatures(
                    start_field=FieldName.START,
                    target_field=FieldName.TARGET,
                    output_field=FieldName.FEAT_TIME,
                    time_features=self.time_features,
                    pred_length=self.prediction_length,
                ),
                AddAgeFeature(
                    target_field=FieldName.TARGET,
                    output_field=FieldName.FEAT_AGE,
                    pred_length=self.prediction_length,
                    log_scale=True,
                ),
                VstackFeatures(
                    output_field=FieldName.FEAT_TIME,
                    input_fields=[FieldName.FEAT_TIME, FieldName.FEAT_AGE]
                    + (
                        [FieldName.FEAT_DYNAMIC_REAL]
                        if self.use_feat_dynamic_real
                        else []
                    ),
                ),
                CanonicalInstanceSplitter(
                    target_field=FieldName.TARGET,
                    is_pad_field=FieldName.IS_PAD,
                    start_field=FieldName.START,
                    forecast_start_field=FieldName.FORECAST_START,
                    instance_sampler=TestSplitSampler(),
                    time_series_fields=[
                        FieldName.FEAT_TIME,
                        SEASON_INDICATORS_FIELD,
github awslabs / gluon-ts / src / gluonts / model / canonical / _estimator.py View on Github external
def create_transformation(self) -> Transformation:
        return Chain(
            trans=[
                AsNumpyArray(field=FieldName.TARGET, expected_ndim=1),
                AddTimeFeatures(
                    start_field=FieldName.START,
                    target_field=FieldName.TARGET,
                    output_field=FieldName.FEAT_TIME,
                    time_features=time_features_from_frequency_str(self.freq),
                    pred_length=self.prediction_length,
                ),
                SetFieldIfNotPresent(
                    field=FieldName.FEAT_STATIC_CAT, value=[0.0]
                ),
                AsNumpyArray(field=FieldName.FEAT_STATIC_CAT, expected_ndim=1),
                InstanceSplitter(
                    target_field=FieldName.TARGET,
                    is_pad_field=FieldName.IS_PAD,
                    start_field=FieldName.START,
                    forecast_start_field=FieldName.FORECAST_START,
                    train_sampler=TestSplitSampler(),
                    time_series_fields=[FieldName.FEAT_TIME],
                    past_length=self.context_length,
                    future_length=self.prediction_length,
github awslabs / gluon-ts / src / gluonts / model / deepstate / _estimator.py View on Github external
AddObservedValuesIndicator(
                    target_field=FieldName.TARGET,
                    output_field=FieldName.OBSERVED_VALUES,
                ),
                # Unnormalized seasonal features
                AddTimeFeatures(
                    time_features=CompositeISSM.seasonal_features(self.freq),
                    pred_length=self.prediction_length,
                    start_field=FieldName.START,
                    target_field=FieldName.TARGET,
                    output_field=SEASON_INDICATORS_FIELD,
                ),
                AddTimeFeatures(
                    start_field=FieldName.START,
                    target_field=FieldName.TARGET,
                    output_field=FieldName.FEAT_TIME,
                    time_features=self.time_features,
                    pred_length=self.prediction_length,
                ),
                AddAgeFeature(
                    target_field=FieldName.TARGET,
                    output_field=FieldName.FEAT_AGE,
                    pred_length=self.prediction_length,
                    log_scale=True,
                ),
                VstackFeatures(
                    output_field=FieldName.FEAT_TIME,
                    input_fields=[FieldName.FEAT_TIME, FieldName.FEAT_AGE]
                    + (
                        [FieldName.FEAT_DYNAMIC_REAL]
                        if self.use_feat_dynamic_real
                        else []

gluonts

Probabilistic time series modeling in Python.

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Apache-2.0
Latest version published 5 months ago

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