diff --git a/README.md b/README.md
index 9d2b525..1dbca4a 100644
--- a/README.md
+++ b/README.md
@@ -89,7 +89,7 @@ run_forecast(
 #### Parameters description:
 
 - ```train_data``` is a delta table name that stores the input dataset.
-- ```scoring_data``` is a delta table name that stores the [dynamic future regressors](https://nixtlaverse.nixtla.io/neuralforecast/examples/exogenous_variables.html#3-training-with-exogenous-variables). If not provided or if the same name as ```train_data``` is provided, the models will ignore the future dynamical regressors. 
+- ```scoring_data``` is a delta table name that stores the [dynamic future regressors](https://nixtlaverse.nixtla.io/statsforecast/docs/how-to-guides/exogenous.html). If not provided or if the same name as ```train_data``` is provided, the models will ignore the future dynamical regressors. 
 - ```scoring_output``` is a delta table where you write your forecasting output. This table will be created if does not exist
 - ```evaluation_output``` is a delta table where you write the evaluation results from all backtesting trials from all time series and all models. This table will be created if does not exist.
 - ```group_id``` is a column storing the unique id that groups your dataset to each time series.
diff --git a/examples/global_external_regressors_daily.py b/examples/global_external_regressors_daily.py
index b9f2dc2..0135e9a 100644
--- a/examples/global_external_regressors_daily.py
+++ b/examples/global_external_regressors_daily.py
@@ -90,7 +90,7 @@
 # COMMAND ----------
 
 # MAGIC %md
-# MAGIC Note that in `rossmann_daily_train` we have our target variable `Sales` but not in `rossmann_daily_test`. This is because `rossmann_daily_test` is going to be used as our `scoring_data` that stores `dynamic_future` variables of the future dates. When you adapt this notebook to your use case, make sure to comply with these datasets formats. See neuralforecast's [documentation](https://nixtlaverse.nixtla.io/neuralforecast/examples/exogenous_variables.html) for more detail on exogenous regressors.
+# MAGIC Note that in `rossmann_daily_train` we have our target variable `Sales` but not in `rossmann_daily_test`. This is because `rossmann_daily_test` is going to be used as our `scoring_data` that stores `dynamic_future_categorical` variables of the future dates. When you adapt this notebook to your use case, make sure to comply with these datasets formats. See neuralforecast's [documentation](https://nixtlaverse.nixtla.io/neuralforecast/examples/exogenous_variables.html) for more detail on exogenous regressors.
 
 # COMMAND ----------
 
@@ -118,7 +118,7 @@
 
 # MAGIC %md ### Run MMF
 # MAGIC
-# MAGIC Now, we run the evaluation and forecasting using `run_forecast` function. We are providing the training table and the scoring table names. If `scoring_data` is not provided or if the same name as `train_data` is provided, the models will ignore the `dynamic_future` regressors. Note that we are providing a covariate field (i.e. `dynamic_future`) this time in `run_forecast` function called in [examples/run_external_regressors_daily.py](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/run_external_regressors_daily.py). There are also other convariate fields, namely `static_features`, and `dynamic_historical`, which you can provide. Read more about these covariates in [neuralforecast's documentation](https://nixtlaverse.nixtla.io/neuralforecast/examples/exogenous_variables.html).
+# MAGIC Now, we run the evaluation and forecasting using `run_forecast` function. We are providing the training table and the scoring table names. If `scoring_data` is not provided or if the same name as `train_data` is provided, the models will ignore the `dynamic_future_numerical` and `dynamic_future_categorical` regressors. Note that we are providing a covariate field (i.e. `dynamic_future_numerical` or `dynamic_future_categorical`) this time in `run_forecast` function called in [examples/run_external_regressors_daily.py](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/run_external_regressors_daily.py). There are also other convariate fields, namely `static_features`, and `dynamic_historical_numerical` and `dynamic_historical_categorical`, which you can provide. Read more about these covariates in [neuralforecast's documentation](https://nixtlaverse.nixtla.io/neuralforecast/examples/exogenous_variables.html).
 
 # COMMAND ----------
 
diff --git a/examples/local_univariate_external_regressors_daily.py b/examples/local_univariate_external_regressors_daily.py
index 0408c4a..0206d45 100644
--- a/examples/local_univariate_external_regressors_daily.py
+++ b/examples/local_univariate_external_regressors_daily.py
@@ -90,7 +90,7 @@
 # COMMAND ----------
 
 # MAGIC %md
-# MAGIC Note that in `rossmann_daily_train` we have our target variable `Sales` but not in `rossmann_daily_test`. This is because `rossmann_daily_test` is going to be used as our `scoring_data` that stores `dynamic_future` variables of the future dates. When you adapt this notebook to your use case, make sure to comply with these datasets formats. See statsforecast's [documentation](https://nixtlaverse.nixtla.io/statsforecast/docs/how-to-guides/exogenous.html) for more detail on exogenous regressors.
+# MAGIC Note that in `rossmann_daily_train` we have our target variable `Sales` but not in `rossmann_daily_test`. This is because `rossmann_daily_test` is going to be used as our `scoring_data` that stores `dynamic_future_categorical` variables of the future dates. When you adapt this notebook to your use case, make sure to comply with these datasets formats. See statsforecast's [documentation](https://nixtlaverse.nixtla.io/statsforecast/docs/how-to-guides/exogenous.html) for more detail on exogenous regressors.
 
 # COMMAND ----------
 
@@ -134,7 +134,7 @@
 
 # MAGIC %md ### Run MMF
 # MAGIC
-# MAGIC Now, we run the evaluation and forecasting using `run_forecast` function. We are providing the training table and the scoring table names. If `scoring_data` is not provided or if the same name as `train_data` is provided, the models will ignore the `dynamic_future` regressors. Note that we are providing a covariate field (i.e. `dynamic_future`) this time. There are also other convariate fields, namely `static_features`, and `dynamic_historical`, but these are only relevant with the global models. 
+# MAGIC Now, we run the evaluation and forecasting using `run_forecast` function. We are providing the training table and the scoring table names. If `scoring_data` is not provided or if the same name as `train_data` is provided, the models will ignore the `dynamic_future_numerical` and `dynamic_future_categorical` regressors. Note that we are providing a covariate field (i.e. `dynamic_future_numerical` or `dynamic_future_categorical`) this time. There are also other convariate fields, namely `static_features`, `dynamic_historical_numerical` and `dynamic_historical_categorical`, but these are only relevant with the global models.
 
 # COMMAND ----------
 
@@ -148,7 +148,7 @@
     date_col="Date",
     target="Sales",
     freq="D",
-    dynamic_future=["DayOfWeek", "Open", "Promo", "SchoolHoliday"],
+    dynamic_future_categorical=["DayOfWeek", "Open", "Promo", "SchoolHoliday"],
     prediction_length=10,
     backtest_months=1,
     stride=10,
diff --git a/examples/run_external_regressors_daily.py b/examples/run_external_regressors_daily.py
index b2d96f8..d145540 100644
--- a/examples/run_external_regressors_daily.py
+++ b/examples/run_external_regressors_daily.py
@@ -36,7 +36,7 @@
     date_col="Date",
     target="Sales",
     freq="D",
-    dynamic_future=["DayOfWeek", "Open", "Promo", "SchoolHoliday"],
+    dynamic_future_categorical=["DayOfWeek", "Open", "Promo", "SchoolHoliday"],
     prediction_length=10,
     backtest_months=1,
     stride=10,
diff --git a/mmf_sa/__init__.py b/mmf_sa/__init__.py
index 348bb4a..ab72ac5 100644
--- a/mmf_sa/__init__.py
+++ b/mmf_sa/__init__.py
@@ -28,8 +28,10 @@ def run_forecast(
     model_output: str = None,
     use_case_name: str = None,
     static_features: List[str] = None,
-    dynamic_future: List[str] = None,
-    dynamic_historical: List[str] = None,
+    dynamic_future_numerical: List[str] = None,
+    dynamic_future_categorical: List[str] = None,
+    dynamic_historical_numerical: List[str] = None,
+    dynamic_historical_categorical: List[str] = None,
     active_models: List[str] = None,
     accelerator: str = "cpu",
     backtest_retrain: bool = None,
@@ -63,8 +65,10 @@ def run_forecast(
         model_output (str): A string specifying the output path for the model.
         use_case_name (str): A string specifying the use case name.
         static_features (List[str]): A list of strings specifying the static features.
-        dynamic_future (List[str]): A list of strings specifying the dynamic future features.
-        dynamic_historical (List[str]): A list of strings specifying the dynamic historical features.
+        dynamic_future_numerical (List[str]): A list of strings specifying the dynamic future features that are numerical.
+        dynamic_future_categorical (List[str]): A list of strings specifying the dynamic future features that are categorical.
+        dynamic_historical_numerical (List[str]): A list of strings specifying the dynamic historical features that are numerical.
+        dynamic_historical_categorical (List[str]): A list of strings specifying the dynamic historical features that are categorical.
         active_models (List[str]): A list of strings specifying the active models.
         accelerator (str): A string specifying the accelerator to use: cpu or gpu. Default is cpu.
         backtest_retrain (bool): A boolean specifying whether to retrain the model during backtesting. Currently, not supported.
@@ -137,10 +141,14 @@ def run_forecast(
         _conf["data_quality_check"] = data_quality_check
     if static_features is not None:
         _conf["static_features"] = static_features
-    if dynamic_future is not None:
-        _conf["dynamic_future"] = dynamic_future
-    if dynamic_historical is not None:
-        _conf["dynamic_historical"] = dynamic_historical
+    if dynamic_future_numerical is not None:
+        _conf["dynamic_future_numerical"] = dynamic_future_numerical
+    if dynamic_future_categorical is not None:
+        _conf["dynamic_future_categorical"] = dynamic_future_categorical
+    if dynamic_historical_numerical is not None:
+        _conf["dynamic_historical_numerical"] = dynamic_historical_numerical
+    if dynamic_historical_categorical is not None:
+        _conf["dynamic_historical_categorical"] = dynamic_historical_categorical
     if run_id is not None:
         _conf["run_id"] = run_id
 
diff --git a/mmf_sa/data_quality_checks.py b/mmf_sa/data_quality_checks.py
index e6921db..31592ec 100644
--- a/mmf_sa/data_quality_checks.py
+++ b/mmf_sa/data_quality_checks.py
@@ -43,8 +43,10 @@ def _external_regressors_check(self):
         """
         if (
             self.conf.get("static_features", None)
-            or self.conf.get("dynamic_future", None)
-            or self.conf.get("dynamic_historical", None)
+            or self.conf.get("dynamic_future_numerical", None)
+            or self.conf.get("dynamic_future_categorical", None)
+            or self.conf.get("dynamic_historical_numerical", None)
+            or self.conf.get("dynamic_historical_categorical", None)
         ):
             if self.conf.get("resample"):
                 raise Exception(
@@ -77,19 +79,29 @@ def _multiple_checks(
 
         # 1. Checking for nulls in external regressors
         static_features = conf.get("static_features", None)
-        dynamic_future = conf.get("dynamic_future", None)
-        dynamic_historical = conf.get("dynamic_historical", None)
+        dynamic_future_numerical = conf.get("dynamic_future_numerical", None)
+        dynamic_future_categorical = conf.get("dynamic_future_categorical", None)
+        dynamic_historical_numerical = conf.get("dynamic_historical_numerical", None)
+        dynamic_historical_categorical = conf.get("dynamic_historical_categorical", None)
         if static_features:
             if _df[static_features].isnull().values.any():
-                # Removing: null in static categoricals
+                # Removing: null in static categorical
                 return pd.DataFrame()
-        if dynamic_future:
-            if _df[dynamic_future].isnull().values.any():
-                # Removing: null in dynamic future
+        if dynamic_future_numerical:
+            if _df[dynamic_future_numerical].isnull().values.any():
+                # Removing: null in dynamic future numerical
                 return pd.DataFrame()
-        if dynamic_historical:
-            if _df[dynamic_historical].isnull().values.any():
-                # Removing: null in dynamic historical
+        if dynamic_future_categorical:
+            if _df[dynamic_future_categorical].isnull().values.any():
+                # Removing: null in dynamic future categorical
+                return pd.DataFrame()
+        if dynamic_historical_numerical:
+            if _df[dynamic_historical_numerical].isnull().values.any():
+                # Removing: null in dynamic historical numerical
+                return pd.DataFrame()
+        if dynamic_historical_categorical:
+            if _df[dynamic_historical_categorical].isnull().values.any():
+                # Removing: null in dynamic historical categorical
                 return pd.DataFrame()
 
         # 2. Checking for training period length
diff --git a/mmf_sa/forecasting_conf.yaml b/mmf_sa/forecasting_conf.yaml
index d9ad660..fd11e79 100644
--- a/mmf_sa/forecasting_conf.yaml
+++ b/mmf_sa/forecasting_conf.yaml
@@ -11,12 +11,16 @@ accelerator: cpu
 static_features:
   #- State
 
-dynamic_future:
+dynamic_future_numerical:
+
+dynamic_future_categorical:
   #- Open
   #- Promo
   #- DayOfWeek
 
-dynamic_historical:
+dynamic_historical_numerical:
+
+dynamic_historical_categorical:
 
 active_models:
   - StatsForecastBaselineWindowAverage
diff --git a/mmf_sa/models/models_conf.yaml b/mmf_sa/models/models_conf.yaml
index 93dfe6e..79fbd39 100644
--- a/mmf_sa/models/models_conf.yaml
+++ b/mmf_sa/models/models_conf.yaml
@@ -10,8 +10,10 @@ promoted_props:
   - backtest_months
   - stride
   - static_features
-  - dynamic_future
-  - dynamic_historical
+  - dynamic_future_numerical
+  - dynamic_future_categorical
+  - dynamic_historical_numerical
+  - dynamic_historical_categorical
 
 models:
 
diff --git a/mmf_sa/models/neuralforecast/NeuralForecastPipeline.py b/mmf_sa/models/neuralforecast/NeuralForecastPipeline.py
index 2fbacac..bba16ef 100644
--- a/mmf_sa/models/neuralforecast/NeuralForecastPipeline.py
+++ b/mmf_sa/models/neuralforecast/NeuralForecastPipeline.py
@@ -68,27 +68,28 @@ def prepare_data(self, df: pd.DataFrame, future: bool = False) -> pd.DataFrame:
         if not future:
             # Prepare historical dataframe with or without exogenous regressors for training
             df[self.params.target] = df[self.params.target].clip(0)
-            if 'dynamic_future' in self.params.keys():
+            features = [self.params.group_id, self.params.date_col, self.params.target]
+            if 'dynamic_future_numerical' in self.params.keys():
                 try:
-                    _df = (
-                        df[[self.params.group_id, self.params.date_col, self.params.target]
-                           + self.params.dynamic_future]
-                    )
+                    features = features + self.params.dynamic_future_numerical
                 except Exception as e:
-                    raise Exception(f"Dynamic future regressor columns missing from "
-                                    f"the training dataset: {e}")
-            elif 'dynamic_historical' in self.params.keys():
+                    raise Exception(f"Dynamic future numerical missing: {e}")
+            if 'dynamic_future_categorical' in self.params.keys():
                 try:
-                    _df = (
-                        df[[self.params.group_id, self.params.date_col, self.params.target]
-                           + self.params.dynamic_historical]
-                    )
+                    features = features + self.params.dynamic_future_categorical
                 except Exception as e:
-                    raise Exception(f"Dynamic historical regressor columns missing from "
-                                    f"the training dataset: {e}")
-            else:
-                _df = df[[self.params.group_id, self.params.date_col, self.params.target]]
-
+                    raise Exception(f"Dynamic future categorical missing: {e}")
+            if 'dynamic_historical_numerical' in self.params.keys():
+                try:
+                    features = features + self.params.dynamic_historical_numerical
+                except Exception as e:
+                    raise Exception(f"Dynamic historical numerical missing: {e}")
+            if 'dynamic_historical_categorical' in self.params.keys():
+                try:
+                    features = features + self.params.dynamic_historical_categorical
+                except Exception as e:
+                    raise Exception(f"Dynamic historical categorical missing: {e}")
+            _df = df[features]
             _df = (
                 _df.rename(
                     columns={
@@ -100,16 +101,18 @@ def prepare_data(self, df: pd.DataFrame, future: bool = False) -> pd.DataFrame:
             )
         else:
             # Prepare future dataframe with exogenous regressors for forecasting
-            if 'dynamic_future' in self.params.keys():
+            features = [self.params.group_id, self.params.date_col]
+            if 'dynamic_future_numerical' in self.params.keys():
+                try:
+                    features = features + self.params.dynamic_future_numerical
+                except Exception as e:
+                    raise Exception(f"Dynamic future numerical missing: {e}")
+            if 'dynamic_future_categorical' in self.params.keys():
                 try:
-                    _df = (
-                        df[[self.params.group_id, self.params.date_col]
-                           + self.params.dynamic_future]
-                    )
+                    features = features + self.params.dynamic_future_categorical
                 except Exception as e:
-                    raise Exception(f"Dynamic future regressors missing: {e}")
-            else:
-                _df = df[[self.params.group_id, self.params.date_col]]
+                    raise Exception(f"Dynamic future categorical missing: {e}")
+            _df = df[features]
             _df = (
                 _df.rename(
                     columns={
@@ -118,7 +121,6 @@ def prepare_data(self, df: pd.DataFrame, future: bool = False) -> pd.DataFrame:
                     }
                 )
             )
-
         return _df
 
     def prepare_static_features(self, df: pd.DataFrame) -> pd.DataFrame:
@@ -284,8 +286,12 @@ def __init__(self, params):
                     decoder_layers=self.params.decoder_layers,
                     learning_rate=self.params.learning_rate,
                     stat_exog_list=list(self.params.get("static_features", [])),
-                    futr_exog_list=list(self.params.get("dynamic_future", [])),
-                    hist_exog_list=list(self.params.get("dynamic_historical", [])),
+                    futr_exog_list=list(
+                        self.params.get("dynamic_future_numerical", []) + self.params.get("dynamic_future_categorical", [])
+                    ),
+                    hist_exog_list=list(
+                        self.params.get("dynamic_historical_numerical", []) + self.params.get("dynamic_historical_categorical", [])
+                    ),
                     accelerator=self.params.accelerator,
                     devices=self.devices,
                 ),
@@ -317,8 +323,12 @@ def __init__(self, params):
                     decoder_layers=self.params.decoder_layers,
                     learning_rate=self.params.learning_rate,
                     stat_exog_list=list(self.params.get("static_features", [])),
-                    futr_exog_list=list(self.params.get("dynamic_future", [])),
-                    hist_exog_list=list(self.params.get("dynamic_historical", [])),
+                    futr_exog_list=list(
+                        self.params.get("dynamic_future_numerical", []) + self.params.get("dynamic_future_categorical", [])
+                    ),
+                    hist_exog_list=list(
+                        self.params.get("dynamic_historical_numerical", []) + self.params.get("dynamic_historical_categorical", [])
+                    ),
                     accelerator=self.params.accelerator,
                     devices=self.devices,
                 ),
@@ -347,8 +357,12 @@ def __init__(self, params):
                     n_polynomials=self.params.n_polynomials,
                     dropout_prob_theta=self.params.dropout_prob_theta,
                     stat_exog_list=list(self.params.get("static_features", [])),
-                    futr_exog_list=list(self.params.get("dynamic_future", [])),
-                    hist_exog_list=list(self.params.get("dynamic_historical", [])),
+                    futr_exog_list=list(
+                        self.params.get("dynamic_future_numerical", []) + self.params.get("dynamic_future_categorical", [])
+                    ),
+                    hist_exog_list=list(
+                        self.params.get("dynamic_historical_numerical", []) + self.params.get("dynamic_historical_categorical", [])
+                    ),
                     accelerator=self.params.accelerator,
                     devices=self.devices,
                 ),
@@ -381,8 +395,12 @@ def __init__(self, params):
                     interpolation_mode=self.params.interpolation_mode,
                     pooling_mode=self.params.pooling_mode,
                     stat_exog_list=list(self.params.get("static_features", [])),
-                    futr_exog_list=list(self.params.get("dynamic_future", [])),
-                    hist_exog_list=list(self.params.get("dynamic_historical", [])),
+                    futr_exog_list=list(
+                        self.params.get("dynamic_future_numerical", []) + self.params.get("dynamic_future_categorical", [])
+                    ),
+                    hist_exog_list=list(
+                        self.params.get("dynamic_historical_numerical", []) + self.params.get("dynamic_historical_categorical", [])
+                    ),
                     accelerator=self.params.accelerator,
                     devices=self.devices,
                 ),
@@ -406,8 +424,12 @@ def __init__(self, params):
         )
         self.exogs = {
             'stat_exog_list': list(self.params.get("static_features", [])),
-            'futr_exog_list': list(self.params.get("dynamic_future", [])),
-            'hist_exog_list': list(self.params.get("dynamic_historical", [])),
+            'futr_exog_list': list(
+                self.params.get("dynamic_future_numerical", []) + self.params.get("dynamic_future_categorical", [])
+            ),
+            'hist_exog_list': list(
+                self.params.get("dynamic_historical_numerical", []) + self.params.get("dynamic_historical_categorical", [])
+            ),
         }
 
         def config(trial):
@@ -459,8 +481,12 @@ def __init__(self, params):
         )
         self.exogs = {
             'stat_exog_list': list(self.params.get("static_features", [])),
-            'futr_exog_list': list(self.params.get("dynamic_future", [])),
-            'hist_exog_list': list(self.params.get("dynamic_historical", [])),
+            'futr_exog_list': list(
+                self.params.get("dynamic_future_numerical", []) + self.params.get("dynamic_future_categorical", [])
+            ),
+            'hist_exog_list': list(
+                self.params.get("dynamic_historical_numerical", []) + self.params.get("dynamic_historical_categorical", [])
+            ),
         }
 
         def config(trial):
@@ -511,8 +537,12 @@ def __init__(self, params):
         )
         self.exogs = {
             'stat_exog_list': list(self.params.get("static_features", [])),
-            'futr_exog_list': list(self.params.get("dynamic_future", [])),
-            'hist_exog_list': list(self.params.get("dynamic_historical", [])),
+            'futr_exog_list': list(
+                self.params.get("dynamic_future_numerical", []) + self.params.get("dynamic_future_categorical", [])
+            ),
+            'hist_exog_list': list(
+                self.params.get("dynamic_historical_numerical", []) + self.params.get("dynamic_historical_categorical", [])
+            ),
         }
 
         def config(trial):
@@ -558,8 +588,12 @@ def __init__(self, params):
         )
         self.exogs = {
             'stat_exog_list': list(self.params.get("static_features", [])),
-            'futr_exog_list': list(self.params.get("dynamic_future", [])),
-            'hist_exog_list': list(self.params.get("dynamic_historical", [])),
+            'futr_exog_list': list(
+                self.params.get("dynamic_future_numerical", []) + self.params.get("dynamic_future_categorical", [])
+            ),
+            'hist_exog_list': list(
+                self.params.get("dynamic_historical_numerical", []) + self.params.get("dynamic_historical_categorical", [])
+            ),
         }
 
         def config(trial):
@@ -608,8 +642,12 @@ def __init__(self, params):
         )
         self.exogs = {
             'stat_exog_list': list(self.params.get("static_features", [])),
-            'futr_exog_list': list(self.params.get("dynamic_future", [])),
-            'hist_exog_list': list(self.params.get("dynamic_historical", [])),
+            'futr_exog_list': list(
+                self.params.get("dynamic_future_numerical", []) + self.params.get("dynamic_future_categorical", [])
+            ),
+            'hist_exog_list': list(
+                self.params.get("dynamic_historical_numerical", []) + self.params.get("dynamic_historical_categorical", [])
+            ),
         }
 
         def config(trial):
@@ -669,8 +707,12 @@ def __init__(self, params):
         )
         self.exogs = {
             'stat_exog_list': list(self.params.get("static_features", [])),
-            'futr_exog_list': list(self.params.get("dynamic_future", [])),
-            'hist_exog_list': list(self.params.get("dynamic_historical", [])),
+            'futr_exog_list': list(
+                self.params.get("dynamic_future_numerical", []) + self.params.get("dynamic_future_categorical", [])
+            ),
+            'hist_exog_list': list(
+                self.params.get("dynamic_historical_numerical", []) + self.params.get("dynamic_historical_categorical", [])
+            ),
         }
 
         def config(trial):
diff --git a/mmf_sa/models/statsforecast/StatsFcForecastingPipeline.py b/mmf_sa/models/statsforecast/StatsFcForecastingPipeline.py
index 64cdc90..26825b6 100644
--- a/mmf_sa/models/statsforecast/StatsFcForecastingPipeline.py
+++ b/mmf_sa/models/statsforecast/StatsFcForecastingPipeline.py
@@ -32,19 +32,20 @@ def prepare_data(self, df: pd.DataFrame, future: bool = False) -> pd.DataFrame:
             # Prepare historical dataframe with/out exogenous regressors for training
             # Fix here
             df[self.params.target] = df[self.params.target].clip(0)
-            if 'dynamic_future' in self.params.keys():
+            features = [self.params.group_id, self.params.date_col, self.params.target]
+            if 'dynamic_future_numerical' in self.params.keys():
                 try:
-                    df_statsfc = (
-                        df[[self.params.group_id, self.params.date_col, self.params.target]
-                           + self.params.dynamic_future]
-                    )
+                    features = features + self.params.dynamic_future_numerical
                 except Exception as e:
-                    raise Exception(f"Exogenous regressors missing: {e}")
-            else:
-                df_statsfc = df[[self.params.group_id, self.params.date_col, self.params.target]]
-
-            df_statsfc = (
-                df_statsfc.rename(
+                    raise Exception(f"Dynamic future numerical missing: {e}")
+            if 'dynamic_future_categorical' in self.params.keys():
+                try:
+                    features = features + self.params.dynamic_future_categorical
+                except Exception as e:
+                    raise Exception(f"Dynamic future categorical missing: {e}")
+            _df = df[features]
+            _df = (
+                _df.rename(
                     columns={
                         self.params.group_id: "unique_id",
                         self.params.date_col: "ds",
@@ -54,26 +55,27 @@ def prepare_data(self, df: pd.DataFrame, future: bool = False) -> pd.DataFrame:
             )
         else:
             # Prepare future dataframe with/out exogenous regressors for forecasting
-            if 'dynamic_future' in self.params.keys():
+            features = [self.params.group_id, self.params.date_col]
+            if 'dynamic_future_numerical' in self.params.keys():
                 try:
-                    df_statsfc = (
-                        df[[self.params.group_id, self.params.date_col]
-                           + self.params.dynamic_future]
-                    )
+                    features = features + self.params.dynamic_future_numerical
                 except Exception as e:
-                    raise Exception(f"Exogenous regressors missing: {e}")
-            else:
-                df_statsfc = df[[self.params.group_id, self.params.date_col]]
-
-            df_statsfc = (
-                df_statsfc.rename(
+                    raise Exception(f"Dynamic future numerical missing: {e}")
+            if 'dynamic_future_categorical' in self.params.keys():
+                try:
+                    features = features + self.params.dynamic_future_categorical
+                except Exception as e:
+                    raise Exception(f"Dynamic future categorical missing: {e}")
+            _df = df[features]
+            _df = (
+                _df.rename(
                     columns={
                         self.params.group_id: "unique_id",
                         self.params.date_col: "ds",
                     }
                 )
             )
-        return df_statsfc
+        return _df
 
     def fit(self, x, y=None):
         self.model = StatsForecast(models=[self.model_spec], freq=self.freq, n_jobs=-1)
@@ -104,7 +106,7 @@ def forecast(self, df: pd.DataFrame, spark=None):
         _df = df[df[self.params.target].notnull()]
         _df = self.prepare_data(_df)
         self.fit(_df)
-        if 'dynamic_future' in self.params.keys():
+        if 'dynamic_future_numerical' in self.params.keys() or 'dynamic_future_categorical' in self.params.keys():
             _last_date = _df["ds"].max()
             _future_df = df[
                 (df[self.params["date_col"]] > np.datetime64(_last_date))
@@ -124,8 +126,7 @@ def forecast(self, df: pd.DataFrame, spark=None):
         else:
             forecast_df = self.model.predict(self.params["prediction_length"])
 
-        target = [col for col in forecast_df.columns.to_list()
-                       if col not in ["unique_id", "ds"]][0]
+        target = [col for col in forecast_df.columns.to_list() if col not in ["unique_id", "ds"]][0]
         forecast_df = forecast_df.reset_index(drop=True).rename(
             columns={
                 "unique_id": self.params.group_id,
diff --git a/tests/unit/test_exogenous_regressors_pipeline.py b/tests/unit/test_exogenous_regressors_pipeline.py
index 060253b..272b7e5 100644
--- a/tests/unit/test_exogenous_regressors_pipeline.py
+++ b/tests/unit/test_exogenous_regressors_pipeline.py
@@ -17,7 +17,7 @@ def base_config():
             "prediction_length": 10,
             "metric": "smape",
             "active_models": ["StatsForecastAutoArima"],
-            "dynamic_reals": ["feature1", "feature2"]
+            "dynamic_future_numerical": ["feature1", "feature2"]
         }
     )
 
diff --git a/tests/unit/test_pipelines.py b/tests/unit/test_pipelines.py
index fe9c693..0cb1d2d 100644
--- a/tests/unit/test_pipelines.py
+++ b/tests/unit/test_pipelines.py
@@ -26,8 +26,10 @@ def test_api_func(temp_dir, spark_session, m4_df, m4_df_exogenous):
         date_col="ds",
         target="y",
         freq="D",
-        dynamic_future=[],
-        dynamic_historical=[],
+        dynamic_future_numerical=[],
+        dynamic_future_categorical=[],
+        dynamic_historical_numerical=[],
+        dynamic_historical_categorical=[],
         static_features=[],
         train_predict_ratio=2,
         active_models=active_models,
@@ -59,8 +61,8 @@ def test_api_func(temp_dir, spark_session, m4_df, m4_df_exogenous):
         date_col="ds",
         target="y",
         freq="D",
-        dynamic_future=["feature1", "feature2"],
-        dynamic_historical=[],
+        dynamic_future_categorical=["feature1", "feature2"],
+        dynamic_historical_categorical=[],
         static_features=[],
         train_predict_ratio=2,
         active_models=active_models,