updated test to infer type

evalml/pipelines/multiseries_regression_pipeline.py

@@ -128,7 +128,9 @@ def predict_in_sample(
             self.time_index,
             self.input_target_name,
         )
+
         # Order series columns to be same as expected input feature names
+        # and filter to only include features in `X_unstacked`.
         input_features = list(self.input_feature_names.values())[0]
         X_unstacked = X_unstacked[
             [feature for feature in input_features if feature in X_unstacked.columns]

evalml/tests/pipeline_tests/regression_pipeline_tests/test_multiseries_regression_pipeline.py

@@ -8,6 +8,7 @@
 from evalml.pipelines import MultiseriesRegressionPipeline
 from evalml.pipelines.utils import unstack_multiseries
 from evalml.preprocessing import split_multiseries_data
+from evalml.utils import infer_feature_types
 
 
 @pytest.fixture(scope="module")
@@ -123,6 +124,7 @@ def test_multiseries_pipeline_predict_in_sample(
     )
     if include_series_id:
         expected = pd.concat([X_holdout["series_id"], expected], axis=1)
+        expected = infer_feature_types(expected)
         pd.testing.assert_frame_equal(y_pred, expected)
     else:
         pd.testing.assert_series_equal(y_pred, expected)