diff --git a/0_meal_identification/meal_identification/meal_identification/transformations/__init__.py b/0_meal_identification/meal_identification/meal_identification/transformations/__init__.py
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index 0000000..e69de29
diff --git a/0_meal_identification/meal_identification/meal_identification/transformations/data_transformations.py b/0_meal_identification/meal_identification/meal_identification/transformations/data_transformations.py
new file mode 100644
index 0000000..bcc11ec
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+++ b/0_meal_identification/meal_identification/meal_identification/transformations/data_transformations.py
@@ -0,0 +1,31 @@
+from sktime.transformations.series.exponent import ExponentTransformer
+from sktime.transformations.compose import Id
+from sktime.transformations.compose import TransformerPipeline
+from sklearn.preprocessing import StandardScaler
+import pandas as pd
+
+def run_pipeline(pipeline, data):
+    '''
+    run a transformer pipeline given certain data
+    questions:  does the training script provide the pipeline, or should we build this in transformation
+                do we want to work with only Series->Series or also Panel->Panel
+    todo:   add flag to cache transformed data
+            add flag to run as list of Series or as Panel
+            log transformations applied
+            testing
+    '''
+    transformed_data = []
+    for df in data:
+        transformed = pipeline.fit_transform(df)
+        transformed_data.append(transformed)
+    return transformed_data
+
+def create_pipeline(transformers):
+    '''
+    creates a pipeline from a list of transformers (apply all in series)?
+    questions:  how should FeatureUnions be handled?
+    '''
+    pipeline = TransformerPipeline(steps=transformers)
+    return pipeline
+
+