How to use the gluonts.transform.AddTimeFeatures function in gluonts
To help you get started, we’ve selected a few gluonts examples, based on popular ways it is used in public projects.
Secure your code as it's written. Use Snyk Code to scan source code in minutes - no build needed - and fix issues immediately.
awslabs / gluon-ts / test / test_transform.py View on Github 
second_dim[0] = "NaN"
ds = gluonts.dataset.common.ListDataset(
data_iter=[{"start": "2012-01-01", "target": [first_dim, second_dim]}],
freq="1D",
one_dim_target=False,
)
pred_length = 2
# Looks weird - but this is necessary to assert the nan entries correctly.
first_dim[-1] = np.nan
second_dim[0] = np.nan
t = transform.Chain(
trans=[
transform.AddTimeFeatures(
start_field=FieldName.START,
target_field=FieldName.TARGET,
output_field="time_feat",
time_features=[
time_feature.DayOfWeek(),
time_feature.DayOfMonth(),
time_feature.MonthOfYear(),
],
pred_length=pred_length,
),
transform.AddAgeFeature(
target_field=FieldName.TARGET,
output_field="age",
pred_length=pred_length,
log_scale=True,
),def test_AddTimeFeatures_empty_time_features(start, target, is_train: bool):
pred_length = 13
t = transform.AddTimeFeatures(
start_field=FieldName.START,
target_field=FieldName.TARGET,
output_field="myout",
pred_length=pred_length,
time_features=[],
)
assert_serializable(t)
data = {"start": start, "target": target}
res = t.map_transform(data, is_train=is_train)
assert res["myout"] is Nonedef test_Transformation():
train_length = 100
ds = gluonts.dataset.common.ListDataset(
[{"start": "2012-01-01", "target": [0.2] * train_length}], freq="1D"
)
pred_length = 10
t = transform.Chain(
trans=[
transform.AddTimeFeatures(
start_field=FieldName.START,
target_field=FieldName.TARGET,
output_field="time_feat",
time_features=[
time_feature.DayOfWeek(),
time_feature.DayOfMonth(),
time_feature.MonthOfYear(),
],
pred_length=pred_length,
),
transform.AddAgeFeature(
target_field=FieldName.TARGET,
output_field="age",
pred_length=pred_length,
log_scale=True,
),field=FieldName.FEAT_STATIC_REAL,
expected_ndim=1,
dtype=self.dtype,
),
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]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
),[SetField(output_field=FieldName.FEAT_STATIC_CAT, value=[0.0])]
if not self.use_feat_static_cat
else []
)
+ [
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]
+ (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(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(),+ (
[SetField(output_field=FieldName.FEAT_STATIC_CAT, value=[0.0])]
if not self.use_feat_static_cat
else []
)
+ [
AsNumpyArray(field=FieldName.FEAT_STATIC_CAT, expected_ndim=1),
AsNumpyArray(field=FieldName.TARGET, expected_ndim=1),
# gives target the (1, T) layout
ExpandDimArray(field=FieldName.TARGET, axis=0),
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,gluonts
Probabilistic time series modeling in Python.
Package Health Score
84 / 100Popular gluonts functions
- gluonts.core.component.validated
- gluonts.core.fqname_for
- gluonts.core.serde.dump_code
- gluonts.core.serde.dump_json
- gluonts.core.serde.load_json
- gluonts.dataset.artificial._base.ConstantDataset
- gluonts.dataset.common.ListDataset
- gluonts.dataset.field_names.FieldName
- gluonts.dataset.field_names.FieldName.FEAT_STATIC_CAT
- gluonts.dataset.field_names.FieldName.FEAT_TIME
- gluonts.dataset.field_names.FieldName.FORECAST_START
- gluonts.dataset.field_names.FieldName.START
- gluonts.dataset.field_names.FieldName.TARGET
- gluonts.distribution.distribution.getF
- gluonts.model.forecast.SampleForecast
- gluonts.support.util.assert_shape
- gluonts.trainer.Trainer
- gluonts.transform.AddTimeFeatures
- gluonts.transform.Chain
- gluonts.transform.InstanceSplitter