def create_transformation(self) -> transform.Transformation:
return transform.Chain(
trans=[
transform.AsNumpyArray(
field=FieldName.TARGET, expected_ndim=1
),
transform.AddTimeFeatures(
start_field=transform.FieldName.START,
target_field=transform.FieldName.TARGET,
output_field=transform.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=transform.FieldName.TARGET,
is_pad_field=transform.FieldName.IS_PAD,
start_field=transform.FieldName.START,
forecast_start_field=transform.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],
),
]
)
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
),
]
)