Fix Rotbaum to handle short series (awslabs#3073)

src/gluonts/ext/rotbaum/_preprocess.py

@@ -452,9 +452,12 @@ def make_features(self, time_series: Dict, starting_index: int) -> List:
         end_index = starting_index + self.context_window_size
         if starting_index < 0:
             prefix = [None] * abs(starting_index)
+            time_series_window = time_series["target"]
         else:
             prefix = []
-        time_series_window = time_series["target"][starting_index:end_index]
+            time_series_window = time_series["target"][
+                starting_index:end_index
+            ]
         only_lag_features, transform_dict = self._pre_transform(
             time_series_window, self.subtract_mean, self.count_nans
         )
@@ -480,10 +483,10 @@ def make_features(self, time_series: Dict, starting_index: int) -> List:
             list(
                 chain(
                     *[
-                        list(ent[0]) + list(ent[1].values())
+                        prefix + list(ent[0]) + list(ent[1].values())
                         for ent in [
                             self._pre_transform(
-                                ts[starting_index:end_index],
+                                ts if prefix else ts[starting_index:end_index],
                                 self.subtract_mean,
                                 self.count_nans,
                             )

test/ext/rotbaum/test_rotbaum_smoke.py

@@ -12,10 +12,12 @@
 # permissions and limitations under the License.
 
 import pytest
+import numpy as np
 
-from gluonts.ext.rotbaum import TreeEstimator
+from gluonts.ext.rotbaum import TreeEstimator, TreePredictor
 
 from gluonts.testutil.dummy_datasets import make_dummy_datasets_with_features
+from gluonts.dataset.common import ListDataset
 
 # TODO: Add support for categorical and dynamic features.
 
@@ -59,3 +61,68 @@ def test_rotbaum_smoke(datasets):
     predictor = estimator.train(dataset_train)
     forecasts = list(predictor.predict(dataset_test))
     assert len(forecasts) == len(dataset_test)
+
+
+def test_short_history_item_pred():
+    prediction_length = 7
+    freq = "D"
+
+    dataset = ListDataset(
+        data_iter=[
+            {
+                "start": "2017-10-11",
+                "item_id": "item_1",
+                "target": np.array(
+                    [
+                        1.0,
+                        9.0,
+                        2.0,
+                        0.0,
+                        0.0,
+                        1.0,
+                        5.0,
+                        3.0,
+                        4.0,
+                        2.0,
+                        0.0,
+                        0.0,
+                        1.0,
+                        6.0,
+                    ]
+                ),
+                "feat_static_cat": np.array([0.0, 0.0], dtype=float),
+                "past_feat_dynamic_real": np.array(
+                    [
+                        [1.0222e06 for i in range(14)],
+                        [750.0 for i in range(14)],
+                    ]
+                ),
+            },
+            {
+                "start": "2017-10-11",
+                "item_id": "item_2",
+                "target": np.array([7.0, 0.0, 0.0, 23.0, 13.0]),
+                "feat_static_cat": np.array([0.0, 1.0], dtype=float),
+                "past_feat_dynamic_real": np.array(
+                    [[0 for i in range(5)], [750.0 for i in range(5)]]
+                ),
+            },
+        ],
+        freq=freq,
+    )
+
+    predictor = TreePredictor(
+        freq=freq,
+        prediction_length=prediction_length,
+        quantiles=[0.1, 0.5, 0.9],
+        max_n_datapts=50000,
+        method="QuantileRegression",
+        use_past_feat_dynamic_real=True,
+        use_feat_dynamic_real=False,
+        use_feat_dynamic_cat=False,
+        use_feat_static_real=False,
+        cardinality="auto",
+    )
+    predictor = predictor.train(dataset)
+    forecasts = list(predictor.predict(dataset))
+    assert forecasts[1].quantile(0.5).shape[0] == prediction_length