diff --git a/analytics/analytics/models/model.py b/analytics/analytics/models/model.py
index 36dbc88..0f8ed13 100644
--- a/analytics/analytics/models/model.py
+++ b/analytics/analytics/models/model.py
@@ -31,7 +31,10 @@ class Model(ABC):
 
                 segment_length = abs(segment_to_index - segment_from_index)
                 segment_length_list.append(segment_length)
-        self.state['WINDOW_SIZE'] = math.ceil(max(segment_length_list) / 2)
+        if len(segment_length_list) > 0:
+            self.state['WINDOW_SIZE'] = math.ceil(max(segment_length_list) / 2)
+        else:
+            self.state['WINDOW_SIZE'] = 0
         self.do_fit(dataframe, segments)
         return self.state
 
diff --git a/analytics/tests/test_dataset.py b/analytics/tests/test_dataset.py
index e6f84fe..fa6d65f 100644
--- a/analytics/tests/test_dataset.py
+++ b/analytics/tests/test_dataset.py
@@ -24,10 +24,5 @@ class TestDataset(unittest.TestCase):
         except ValueError:
             self.fail('Model {} raised unexpectedly'.format(model_name))
 
-    def test_data_preparation(self):
-        data = [[1523889000000 + i, float('nan')] for i in range(10)]
-
-        self.assertTrue(prepare_data(data).empty) # TODO: raise exception
-
 if __name__ == '__main__':
     unittest.main()
diff --git a/analytics/tests/test_utils.py b/analytics/tests/test_utils.py
index 0dde71a..10f3e5c 100644
--- a/analytics/tests/test_utils.py
+++ b/analytics/tests/test_utils.py
@@ -81,9 +81,11 @@ class TestUtils(unittest.TestCase):
         data = []
         pattern_index = []
         window_size = 2
+        window_size_zero = 0
         av_model = []
         result = []
         self.assertEqual(utils.get_convolve(pattern_index, av_model, data, window_size), result)
+        self.assertEqual(utils.get_convolve(pattern_index, av_model, data, window_size_zero), result)
     
     def test_get_distribution_density(self):
         segment = [1, 1, 1, 3, 5, 5, 5]
@@ -95,39 +97,66 @@ class TestUtils(unittest.TestCase):
         segment = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5]
         segment = pd.Series(segment)
         jump_center = [10, 11]
-        self.assertIn(utils.find_jump_parameters(segment, 0)[0], jump_center)
+        self.assertIn(utils.find_parameters(segment, 0, 'jump')[0], jump_center)
     
     def test_find_jump_parameters_height(self):
         segment = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5]
         segment = pd.Series(segment)
         jump_height = [3.5, 4]
-        self.assertGreaterEqual(utils.find_jump_parameters(segment, 0)[1], jump_height[0])
-        self.assertLessEqual(utils.find_jump_parameters(segment, 0)[1], jump_height[1])
+        self.assertGreaterEqual(utils.find_parameters(segment, 0, 'jump')[1], jump_height[0])
+        self.assertLessEqual(utils.find_parameters(segment, 0, 'jump')[1], jump_height[1])
     
     def test_find_jump_parameters_length(self):
         segment = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5]
         segment = pd.Series(segment)
         jump_length = 2
-        self.assertEqual(utils.find_jump_parameters(segment, 0)[2], jump_length)
+        self.assertEqual(utils.find_parameters(segment, 0, 'jump')[2], jump_length)
     
     def test_find_drop_parameters_center(self):
         segment = [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
         segment = pd.Series(segment)
-        drop_center = [14, 15]
-        self.assertIn(utils.find_drop_parameters(segment, 0)[0], drop_center)
+        drop_center = [14, 15, 16]
+        self.assertIn(utils.find_parameters(segment, 0, 'drop')[0], drop_center)
     
     def test_find_drop_parameters_height(self):
         segment = [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
         segment = pd.Series(segment)
         drop_height = [3.5, 4]
-        self.assertGreaterEqual(utils.find_drop_parameters(segment, 0)[1], drop_height[0])
-        self.assertLessEqual(utils.find_drop_parameters(segment, 0)[1], drop_height[1])
+        self.assertGreaterEqual(utils.find_parameters(segment, 0, 'drop')[1], drop_height[0])
+        self.assertLessEqual(utils.find_parameters(segment, 0, 'drop')[1], drop_height[1])
     
     def test_find_drop_parameters_length(self):
         segment = [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
         segment = pd.Series(segment)
         drop_length = 2
-        self.assertEqual(utils.find_drop_parameters(segment, 0)[2], drop_length)
+        self.assertEqual(utils.find_parameters(segment, 0, 'drop')[2], drop_length)
+    
+    def test_get_av_model_empty_data(self):
+        patterns_list = []
+        result = []
+        self.assertEqual(utils.get_av_model(patterns_list), result)
+    
+    def test_find_jump_nan_data(self):
+        data = [np.NaN, np.NaN, np.NaN, np.NaN]
+        data = pd.Series(data)
+        length = 2
+        height = 3
+        length_zero = 0
+        height_zero = 0
+        result = []
+        self.assertEqual(utils.find_jump(data, height, length), result)
+        self.assertEqual(utils.find_jump(data, height_zero, length_zero), result)
+    
+    def test_find_drop_nan_data(self):
+        data = [np.NaN, np.NaN, np.NaN, np.NaN]
+        data = pd.Series(data)
+        length = 2
+        height = 3
+        length_zero = 0
+        height_zero = 0
+        result = []
+        self.assertEqual(utils.find_drop(data, height, length), result)
+        self.assertEqual(utils.find_drop(data, height_zero, length_zero), result)
 
 if __name__ == '__main__':
     unittest.main()