diff --git a/tsfm_public/toolkit/time_series_forecasting_pipeline.py b/tsfm_public/toolkit/time_series_forecasting_pipeline.py
index 39e3e0e5..6f9961da 100644
--- a/tsfm_public/toolkit/time_series_forecasting_pipeline.py
+++ b/tsfm_public/toolkit/time_series_forecasting_pipeline.py
@@ -101,11 +101,10 @@ def run_single(self, inputs, preprocess_params, forward_params, postprocess_para
     build_pipeline_init_args(has_tokenizer=False, has_feature_extractor=True, has_image_processor=False)
 )
 class TimeSeriesForecastingPipeline(TimeSeriesPipeline):
-    """Hugging Face Pipeline for Time Series Forecasting
+    """
+    Time Series Forecasting using HF time series forecasting models. This pipeline consumes a `pandas.DataFrame`
+    containing the time series data and produces a new `pandas.DataFrame` containing the forecasts.
 
-    feature_extractor (TimeSeriesPreprocessor): A time series preprpocessor object that specifies how the time
-            series should be prepared. If this is provided, and of the other options below will be automatically
-            populated from this instance.
     """
 
     def __init__(
@@ -132,7 +131,8 @@ def _sanitize_parameters(
         self,
         **kwargs,
     ):
-        """Assign parameters to the different parts of the process.
+        """Assigns parameters to the different steps of the process. If context_length and prediction_length
+        are not provided they are taken from the model config.
 
         For expected parameters see the call method below.
         """
@@ -140,6 +140,21 @@ def _sanitize_parameters(
         context_length = kwargs.get("context_length", self.model.config.context_length)
         prediction_length = kwargs.get("prediction_length", self.model.config.prediction_length)
 
+        # autopopulate from feature extractor
+        if self.feature_extractor:
+            for p in [
+                "id_columns",
+                "timestamp_column",
+                "target_columns",
+                "observable_columns",
+                "control_columns",
+                "conditional_columns",
+                "static_categorical_columns",
+                "freq",
+            ]:
+                if p not in kwargs:
+                    kwargs[p] = getattr(self.feature_extractor, p)
+
         preprocess_kwargs = {
             "prediction_length": prediction_length,
             "context_length": context_length,
@@ -226,7 +241,7 @@ def __call__(
                 i.e., exogenous or supporting features which are known in advance.
 
             feature_extractor (TimeSeriesPreprocessor): A time series preprpocessor object that specifies how the time
-                series should be prepared. If this is provided, and of the other options below will be automatically
+                series should be prepared. If this is provided, any of the other options below will be automatically
                 populated from this instance.
 
             timestamp_column (str): The name of the column containing the timestamp of the time series.
@@ -267,6 +282,10 @@ def __call__(
 
             inverse_scale_outputs (bool): If true and a valid feature extractor is provided, the outputs will be inverse scaled.
 
+            add_known_ground_truth (bool): If True add columns containing the ground truth data. Prediction columns will have a
+                suffix of "_prediction". Default True. If false, on columns containing predictions are produced, no suffix is
+                added.
+
         Return (pandas dataframe):
             A new pandas dataframe containing the forecasts. Each row will contain the id, timestamp, the original
             input feature values and the output forecast for each input column. The output forecast is a list containing
@@ -286,6 +305,8 @@ def preprocess(self, time_series, **kwargs) -> Dict[str, Union[GenericTensor, Li
         id_columns = kwargs.get("id_columns")
         # context_length = kwargs.get("context_length")
 
+        # use the feature extractor here
+
         if isinstance(time_series, str):
             time_series = pd.read_csv(
                 time_series,
@@ -327,17 +348,6 @@ def preprocess(self, time_series, **kwargs) -> Dict[str, Union[GenericTensor, Li
             **kwargs,
         )
 
-        # # stack all the outputs
-        # # torch tensors are stacked, but other values are passed through as a list
-        # first = dataset[0]
-        # full_output = {}
-        # for k, v in first.items():
-        #     if isinstance(v, torch.Tensor):
-        #         full_output[k] = torch.stack(tuple(r[k] for r in dataset))
-        #     else:
-        #         full_output[k] = [r[k] for r in dataset]
-
-        # return full_output
         return dataset
 
     def _forward(self, model_inputs, **kwargs):