diff --git a/mmf_sa/Forecaster.py b/mmf_sa/Forecaster.py
index 85cc4e0..fac4a96 100644
--- a/mmf_sa/Forecaster.py
+++ b/mmf_sa/Forecaster.py
@@ -378,6 +378,9 @@ def backtest_global_model(
                 StructField("model_pickle", BinaryType()),
             ]
         )
+        # Covert to Python-native types before converting to pyspark dataframe
+        res_pdf['forecast'] = res_pdf['forecast'].apply(lambda x: [float(i) for i in x])
+        res_pdf['actual'] = res_pdf['actual'].apply(lambda x: [float(i) for i in x])
         res_sdf = self.spark.createDataFrame(res_pdf, schema)
         # Write evaluation results to a delta table
         if write:
diff --git a/mmf_sa/models/neuralforecast/NeuralForecastPipeline.py b/mmf_sa/models/neuralforecast/NeuralForecastPipeline.py
index 12697f4..d9c1d2e 100644
--- a/mmf_sa/models/neuralforecast/NeuralForecastPipeline.py
+++ b/mmf_sa/models/neuralforecast/NeuralForecastPipeline.py
@@ -217,9 +217,9 @@ def calculate_metrics(
         else:
             raise Exception(f"Metric {self.params['metric']} not supported!")
         for key in keys:
-            actual = val_df[val_df[self.params["group_id"]] == key][self.params["target"]]
+            actual = val_df[val_df[self.params["group_id"]] == key][self.params["target"]].reset_index(drop=True)
             forecast = pred_df[pred_df[self.params["group_id"]] == key][self.params["target"]].\
-                         iloc[-self.params["prediction_length"]:]
+                         iloc[-self.params["prediction_length"]:].reset_index(drop=True)
             try:
                 if metric_name == "smape":
                     smape = MeanAbsolutePercentageError(symmetric=True)