Index: trunk/SDToolBox/statistical_model.py
===================================================================
diff -u -r96 -r97
--- trunk/SDToolBox/statistical_model.py	(.../statistical_model.py)	(revision 96)
+++ trunk/SDToolBox/statistical_model.py	(.../statistical_model.py)	(revision 97)
@@ -48,9 +48,38 @@
             random_state=0)
 
     @staticmethod
-    def cross_validation(*argv):
-        if(not argv or len(argv) != 4):
-            raise Exception()
-        clf = svm.SVC(kernel='linear', C=1).fit(argv[0], argv[2])
-        print(clf.score(argv[1], argv[3]))
+    def cross_validation(train_test_data: Tuple[list, list, list, list]):
+        """Cross validates the train_test_data from the train_and_validate
+        function and returns  the linear factor of its training data.
+
+        Arguments:
+            train_test_data {Tuple[list, list, list, list]}
+                -- Lon_train, Lon_test, Lat_train, Lat_test
+
+        Raises:
+            Exception: When the argument is not given as expected
+
+        Returns:
+            [type] -- [description]
+        """
+        if(not train_test_data or len(train_test_data) != 4):
+            raise Exception('Not all arguments given.')
+        clf = svm.SVC(
+            kernel='linear',
+            C=1
+            ).fit(
+                train_test_data[0],
+                train_test_data[2])
+        print(clf.score(train_test_data[1], train_test_data[3]))
         return clf
+
+    @staticmethod
+    def time_series_split(
+            train_test_data: Tuple[list, list, list, list],
+            k_splits: int):
+        if(not train_test_data or len(train_test_data) != 4
+                or not k_splits):
+            raise Exception('Not all arguments given.')
+
+        tscv = TimeSeriesSplit(k_splits)
+        return tscv.split(train_test_data)