PatchTST: Add support for time features

src/gluonts/torch/model/patch_tst/estimator.py

@@ -30,6 +30,7 @@
     TestSplitSampler,
     ExpectedNumInstanceSampler,
     SelectFields,
+    RenameFields,
 )
 from gluonts.torch.model.estimator import PyTorchLightningEstimator
 from gluonts.torch.model.predictor import PyTorchPredictor
@@ -74,6 +75,8 @@ class PatchTSTEstimator(PyTorchLightningEstimator):
         Number of attention heads in the Transformer encoder which must divide d_model.
     dim_feedforward
         Size of hidden layers in the Transformer encoder.
+    num_feat_dynamic_real
+        Number of dynamic real features in the data (default: 0).
     dropout
         Dropout probability in the Transformer encoder.
     activation
@@ -115,6 +118,7 @@ def __init__(
         d_model: int = 32,
         nhead: int = 4,
         dim_feedforward: int = 128,
+        num_feat_dynamic_real: int = 0,
         dropout: float = 0.1,
         activation: str = "relu",
         norm_first: bool = False,
@@ -151,6 +155,7 @@ def __init__(
         self.d_model = d_model
         self.nhead = nhead
         self.dim_feedforward = dim_feedforward
+        self.num_feat_dynamic_real = num_feat_dynamic_real
         self.dropout = dropout
         self.activation = activation
         self.norm_first = norm_first
@@ -166,17 +171,26 @@ def __init__(
         )
 
     def create_transformation(self) -> Transformation:
-        return SelectFields(
-            [
-                FieldName.ITEM_ID,
-                FieldName.INFO,
-                FieldName.START,
-                FieldName.TARGET,
-            ],
-            allow_missing=True,
-        ) + AddObservedValuesIndicator(
-            target_field=FieldName.TARGET,
-            output_field=FieldName.OBSERVED_VALUES,
+        return (
+            SelectFields(
+                [
+                    FieldName.ITEM_ID,
+                    FieldName.INFO,
+                    FieldName.START,
+                    FieldName.TARGET,
+                ]
+                + (
+                    [FieldName.FEAT_DYNAMIC_REAL]
+                    if self.num_feat_dynamic_real > 0
+                    else []
+                ),
+                allow_missing=True,
+            )
+            + RenameFields({FieldName.FEAT_DYNAMIC_REAL: FieldName.FEAT_TIME})
+            + AddObservedValuesIndicator(
+                target_field=FieldName.TARGET,
+                output_field=FieldName.OBSERVED_VALUES,
+            )
         )
 
     def create_lightning_module(self) -> pl.LightningModule:
@@ -192,6 +206,7 @@ def create_lightning_module(self) -> pl.LightningModule:
                 "d_model": self.d_model,
                 "nhead": self.nhead,
                 "dim_feedforward": self.dim_feedforward,
+                "num_feat_dynamic_real": self.num_feat_dynamic_real,
                 "dropout": self.dropout,
                 "activation": self.activation,
                 "norm_first": self.norm_first,
@@ -220,7 +235,10 @@ def _create_instance_splitter(
             instance_sampler=instance_sampler,
             past_length=self.context_length,
             future_length=self.prediction_length,
-            time_series_fields=[FieldName.OBSERVED_VALUES],
+            time_series_fields=[FieldName.OBSERVED_VALUES]
+            + (
+                [FieldName.FEAT_TIME] if self.num_feat_dynamic_real > 0 else []
+            ),
             dummy_value=self.distr_output.value_in_support,
         )
 
@@ -239,7 +257,15 @@ def create_training_data_loader(
             instances,
             batch_size=self.batch_size,
             shuffle_buffer_length=shuffle_buffer_length,
-            field_names=TRAINING_INPUT_NAMES,
+            field_names=TRAINING_INPUT_NAMES
+            + (
+                [
+                    f"past_{FieldName.FEAT_TIME}",
+                    f"future_{FieldName.FEAT_TIME}",
+                ]
+                if self.num_feat_dynamic_real > 0
+                else []
+            ),
             output_type=torch.tensor,
             num_batches_per_epoch=self.num_batches_per_epoch,
         )
@@ -253,7 +279,15 @@ def create_validation_data_loader(
         return as_stacked_batches(
             instances,
             batch_size=self.batch_size,
-            field_names=TRAINING_INPUT_NAMES,
+            field_names=TRAINING_INPUT_NAMES
+            + (
+                [
+                    f"past_{FieldName.FEAT_TIME}",
+                    f"future_{FieldName.FEAT_TIME}",
+                ]
+                if self.num_feat_dynamic_real > 0
+                else []
+            ),
             output_type=torch.tensor,
         )
 
@@ -264,7 +298,15 @@ def create_predictor(
 
         return PyTorchPredictor(
             input_transform=transformation + prediction_splitter,
-            input_names=PREDICTION_INPUT_NAMES,
+            input_names=PREDICTION_INPUT_NAMES
+            + (
+                [
+                    f"past_{FieldName.FEAT_TIME}",
+                    f"future_{FieldName.FEAT_TIME}",
+                ]
+                if self.num_feat_dynamic_real > 0
+                else []
+            ),
             prediction_net=module,
             forecast_generator=self.distr_output.forecast_generator,
             batch_size=self.batch_size,

src/gluonts/torch/model/patch_tst/module.py

@@ -11,7 +11,7 @@
 # express or implied. See the License for the specific language governing
 # permissions and limitations under the License.
 
-from typing import Tuple
+from typing import Optional, Tuple
 
 import numpy as np
 import torch
@@ -21,7 +21,7 @@
 from gluonts.model import Input, InputSpec
 from gluonts.torch.distributions import StudentTOutput
 from gluonts.torch.scaler import StdScaler, MeanScaler, NOPScaler
-from gluonts.torch.util import unsqueeze_expand, weighted_average
+from gluonts.torch.util import take_last, unsqueeze_expand, weighted_average
 from gluonts.torch.model.simple_feedforward import make_linear_layer
 
 
@@ -85,6 +85,8 @@ class PatchTSTModel(nn.Module):
         Number of time points to predict.
     context_length
         Number of time steps prior to prediction time that the model.
+    num_feat_dynamic_real
+        Number of dynamic real features in the data (default: 0).
     distr_output
         Distribution to use to evaluate observations and sample predictions.
         Default: ``StudentTOutput()``.
@@ -101,6 +103,7 @@ def __init__(
         d_model: int,
         nhead: int,
         dim_feedforward: int,
+        num_feat_dynamic_real: int,
         dropout: float,
         activation: str,
         norm_first: bool,
@@ -120,6 +123,7 @@ def __init__(
         self.d_model = d_model
         self.padding_patch = padding_patch
         self.distr_output = distr_output
+        self.num_feat_dynamic_real = num_feat_dynamic_real
 
         if scaling == "mean":
             self.scaler = MeanScaler(keepdim=True)
@@ -133,8 +137,11 @@ def __init__(
             self.padding_patch_layer = nn.ReplicationPad1d((0, self.stride))
             self.patch_num += 1
 
-        # project from patch_len + 2 features (loc and scale) to d_model
-        self.patch_proj = make_linear_layer(patch_len + 2, d_model)
+        # project from `patch_len` + 2 features (`loc` and `scale`) +
+        # `num_feat_dynamic_real` x `patch_len` to d_model
+        self.patch_proj = make_linear_layer(
+            patch_len + 2 + self.num_feat_dynamic_real * patch_len, d_model
+        )
 
         self.positional_encoding = SinusoidalPositionalEmbedding(
             self.patch_num, d_model
@@ -163,6 +170,28 @@ def __init__(
         self.args_proj = self.distr_output.get_args_proj(d_model)
 
     def describe_inputs(self, batch_size=1) -> InputSpec:
+        if self.num_feat_dynamic_real > 0:
+            input_spec_feat = {
+                "past_time_feat": Input(
+                    shape=(
+                        batch_size,
+                        self.context_length,
+                        self.num_feat_dynamic_real,
+                    ),
+                    dtype=torch.float,
+                ),
+                "future_time_feat": Input(
+                    shape=(
+                        batch_size,
+                        self.prediction_length,
+                        self.num_feat_dynamic_real,
+                    ),
+                    dtype=torch.float,
+                ),
+            }
+        else:
+            input_spec_feat = {}
+
         return InputSpec(
             {
                 "past_target": Input(
@@ -171,6 +200,7 @@ def describe_inputs(self, batch_size=1) -> InputSpec:
                 "past_observed_values": Input(
                     shape=(batch_size, self.context_length), dtype=torch.float
                 ),
+                **input_spec_feat,
             },
             torch.zeros,
         )
@@ -179,6 +209,8 @@ def forward(
         self,
         past_target: torch.Tensor,
         past_observed_values: torch.Tensor,
+        past_time_feat: Optional[torch.Tensor] = None,
+        future_time_feat: Optional[torch.Tensor] = None,
     ) -> Tuple[Tuple[torch.Tensor, ...], torch.Tensor, torch.Tensor]:
         # scale the input
         past_target_scaled, loc, scale = self.scaler(
@@ -192,6 +224,25 @@ def forward(
             dimension=1, size=self.patch_len, step=self.stride
         )
 
+        # do patching for time features as well
+        if self.num_feat_dynamic_real > 0:
+            # shift time features by `prediction_length` so that they are
+            # aligned with the target input.
+            time_feat = take_last(
+                torch.cat((past_time_feat, future_time_feat), dim=1),
+                dim=1,
+                num=self.context_length,
+            )
+
+            # (bs x T x d) --> (bs x d x T) because the 1D padding is done on
+            # the last dimension.
+            time_feat = self.padding_patch_layer(
+                time_feat.transpose(-2, -1)
+            ).transpose(-2, -1)
+            time_feat_patches = time_feat.unfold(
+                dimension=1, size=self.patch_len, step=self.stride
+            ).flatten(-2, -1)
+
         # add loc and scale to past_target_patches as additional features
         log_abs_loc = loc.abs().log1p()
         log_scale = scale.log()
@@ -202,6 +253,9 @@ def forward(
         )
         inputs = torch.cat((past_target_patches, expanded_static_feat), dim=-1)
 
+        if self.num_feat_dynamic_real > 0:
+            inputs = torch.cat((inputs, time_feat_patches), dim=-1)
+
         # project patches
         enc_in = self.patch_proj(inputs)
         embed_pos = self.positional_encoding(enc_in.size())
@@ -224,9 +278,14 @@ def loss(
         past_observed_values: torch.Tensor,
         future_target: torch.Tensor,
         future_observed_values: torch.Tensor,
+        past_time_feat: Optional[torch.Tensor] = None,
+        future_time_feat: Optional[torch.Tensor] = None,
     ) -> torch.Tensor:
         distr_args, loc, scale = self(
-            past_target=past_target, past_observed_values=past_observed_values
+            past_target=past_target,
+            past_observed_values=past_observed_values,
+            past_time_feat=past_time_feat,
+            future_time_feat=future_time_feat,
         )
         loss = self.distr_output.loss(
             target=future_target, distr_args=distr_args, loc=loc, scale=scale

test/torch/model/test_estimators.py

@@ -296,6 +296,25 @@ def test_estimator_constant_dataset(
             num_batches_per_epoch=3,
             epochs=2,
         ),
+        lambda freq, prediction_length: PatchTSTEstimator(
+            prediction_length=prediction_length,
+            context_length=2 * prediction_length,
+            num_feat_dynamic_real=3,
+            patch_len=16,
+            batch_size=4,
+            num_batches_per_epoch=3,
+            trainer_kwargs=dict(max_epochs=2),
+        ),
+        lambda freq, prediction_length: PatchTSTEstimator(
+            prediction_length=prediction_length,
+            context_length=2 * prediction_length,
+            num_feat_dynamic_real=3,
+            distr_output=QuantileOutput(quantiles=[0.1, 0.6, 0.85]),
+            patch_len=16,
+            batch_size=4,
+            num_batches_per_epoch=3,
+            trainer_kwargs=dict(max_epochs=2),
+        ),
         lambda freq, prediction_length: WaveNetEstimator(
             freq=freq,
             prediction_length=prediction_length,