diff --git a/lightwood/mixer/gluonts.py b/lightwood/mixer/gluonts.py
index 1061241f6..ac7d2bc5f 100644
--- a/lightwood/mixer/gluonts.py
+++ b/lightwood/mixer/gluonts.py
@@ -5,6 +5,7 @@
 import numpy as np
 import pandas as pd
 import mxnet as mx
+from sklearn.preprocessing import OrdinalEncoder
 
 from gluonts.dataset.pandas import PandasDataset
 
@@ -36,6 +37,8 @@ def __init__(
             early_stop_patience: int = 3,
             distribution_output: str = '',
             seed: int = 0,
+            static_features_cat: Optional[list[str]] = None,
+            static_features_real: Optional[list[str]] = None,
     ):
         """
         Wrapper around GluonTS probabilistic deep learning models. For now, only DeepAR is supported.
@@ -71,6 +74,9 @@ def __init__(
         self.patience = early_stop_patience
         self.seed = seed
         self.trains_once = True
+        self.static_features_cat_encoders = {}
+        self.static_features_cat = static_features_cat if static_features_cat else []
+        self.static_features_real = static_features_real if static_features_real else []
 
         dist_module = importlib.import_module('.'.join(['gluonts.mx.distribution',
                                                         *distribution_output.split(".")[:-1]]))
@@ -88,6 +94,9 @@ def fit(self, train_data: EncodedDs, dev_data: EncodedDs) -> None:
 
         # prepare data
         cat_ds = ConcatedEncodedDs([train_data, dev_data])
+        for col in self.static_features_cat:
+            self.static_features_cat_encoders[col] = OrdinalEncoder().fit(cat_ds.data_frame[col].values.reshape(-1, 1))
+
         fit_groups = list(cat_ds.data_frame[self.grouped_by[0]].unique()) if self.grouped_by != ['__default'] else None
         train_ds = self._make_initial_ds(cat_ds.data_frame, phase='train', groups=fit_groups)
         batch_size = 32
@@ -99,6 +108,8 @@ def fit(self, train_data: EncodedDs, dev_data: EncodedDs) -> None:
             distr_output=self.distribution,
             lags_seq=[i + 1 for i in range(self.window)],
             batch_size=batch_size,
+            use_feat_static_cat=True if self.static_features_cat else False,
+            use_feat_static_real=True if self.static_features_real else False,
             trainer=Trainer(
                 epochs=self.n_epochs,
                 num_batches_per_epoch=max(1, len(cat_ds.data_frame) // batch_size),
@@ -171,7 +182,13 @@ def _make_initial_ds(self, df=None, phase='predict', groups=None):
         oby_col_name = '__gluon_timestamp'
         gby = self.ts_analysis["tss"].group_by if self.ts_analysis["tss"].group_by else []
         freq = self.ts_analysis['sample_freqs']['__default']
-        keep_cols = [self.target] + [col for col in gby]
+        keep_cols = [self.target] + [col for col in gby] + self.static_features_cat + self.static_features_real
+
+        agg_map = {self.target: 'sum'}
+        for col in self.static_features_cat:
+            agg_map[col] = 'first'
+        for col in self.static_features_real:
+            agg_map[col] = 'mean'
 
         if groups is None and gby:
             groups = self.groups
@@ -207,15 +224,26 @@ def _make_initial_ds(self, df=None, phase='predict', groups=None):
             return None
 
         if gby:
-            df = df.groupby(by=gby[0]).resample(freq).sum().reset_index(level=[0])
             # @TODO: multiple group support and remove groups without enough data
+            df = df.groupby(by=gby[0]).resample(freq).agg(agg_map).reset_index(level=[0])
         else:
-            df = df.resample(freq).sum()
+            df = df.resample(freq).agg(agg_map)
             gby = '__default_group'
             df[gby] = '__default_group'
 
         df[oby_col_name] = df.index
-        ds = PandasDataset.from_long_dataframe(df, target=self.target, item_id=gby, freq=freq, timestamp=oby_col_name)
+        for col in self.static_features_cat:
+            df[col] = self.static_features_cat_encoders[col].transform(df[col].values.reshape(-1, 1))
+
+        ds = PandasDataset.from_long_dataframe(
+            df,
+            target=self.target,
+            item_id=gby,
+            freq=freq,
+            timestamp=oby_col_name,
+            feat_static_real=self.static_features_real if self.static_features_real else None,
+            feat_static_cat=self.static_features_cat if self.static_features_cat else None,
+        )
         return ds