diff --git a/mlpp_lib/model_selection.py b/mlpp_lib/model_selection.py
index 8d32fea..9d2020b 100644
--- a/mlpp_lib/model_selection.py
+++ b/mlpp_lib/model_selection.py
@@ -114,6 +114,28 @@ def __init__(
         self.seed = seed
         self.time_dim_name = time_dim_name
 
+    def fit(self, *args: xr.Dataset) -> Self:
+        """Compute splits based on the input datasets.
+
+        Parameters
+        ----------
+        *args: `xr.Dataset`
+            The datasets defining the time and station indices.
+
+        Returns
+        -------
+        self: `DataSplitter`
+            The fitted DataSplitter instance.
+        """
+
+        self.time_index = args[0][self.time_dim_name].values.copy()
+        self.station_index = args[0].station.values.copy()
+
+        self._time_partitioning()
+        self._station_partitioning()
+
+        return self
+
     def get_partition(
         self, *args: xr.Dataset, partition=None, thinning: Optional[Mapping] = None
     ) -> tuple[xr.Dataset, ...]:
@@ -141,11 +163,8 @@ def get_partition(
         if partition is None:
             raise ValueError("Keyword argument `partition` must be provided.")
 
-        self.time_index = args[0][self.time_dim_name].values.copy()
-        self.station_index = args[0].station.values.copy()
-
-        self._time_partitioning()
-        self._station_partitioning()
+        if not hasattr(self, "time_index") or not hasattr(self, "station_index"):
+            self = self.fit(*args)
 
         # avoid out-of-order indexing (leads to bad performance with xarray/dask)
         station_idx = self.partitions[partition]["station"]
@@ -289,9 +308,9 @@ def from_dict(cls, splits: dict) -> Self:
         splitter._station_partitioning()
         return splitter
 
-    def to_dict(self):
+    def to_dict(self, sort_values=False):
         if not hasattr(self, "time_index") or not hasattr(self, "station_index"):
-            raise ValueError("DataSplitter wasn't applied on data yet")
+            raise ValueError("DataSplitter wasn't applied on any data yet, run `fit` first.")
         if not hasattr(self, "partitions"):
             self._time_partitioning()
             self._station_partitioning()
@@ -302,6 +321,8 @@ def to_dict(self):
                     partitions[split_key][dim] = [str(value.start), str(value.stop)]
                 elif hasattr(value, "tolist"):
                     partitions[split_key][dim] = value.astype(str).tolist()
+                    if sort_values:
+                        partitions[split_key][dim] = sorted(partitions[split_key][dim])
         return partitions
 
     @classmethod