diff --git a/analytics/models/drop_model.py b/analytics/models/drop_model.py
index 15cb130..065d6a3 100644
--- a/analytics/models/drop_model.py
+++ b/analytics/models/drop_model.py
@@ -101,8 +101,10 @@ class DropModel(Model):
     def do_predict(self, dataframe: pd.DataFrame):
         data = dataframe['value']
         possible_drops = utils.find_drop(data, self.state['DROP_HEIGHT'], self.state['DROP_LENGTH'] + 1)
+
         filtered = self.__filter_prediction(possible_drops, data)
-        return [(dataframe['timestamp'][x - 1].value, dataframe['timestamp'][x + 1].value) for x in filtered]
+        # TODO: convert from ns to ms more proper way (not dividing by 10^6)
+        return [(dataframe['timestamp'][x - 1].value / 1000000, dataframe['timestamp'][x + 1].value / 1000000) for x in filtered]
 
     def __filter_prediction(self, segments: list, data: list):
         delete_list = []
diff --git a/analytics/models/general_model.py b/analytics/models/general_model.py
index 33b5bf4..42ba460 100644
--- a/analytics/models/general_model.py
+++ b/analytics/models/general_model.py
@@ -79,7 +79,8 @@ class GeneralModel(Model):
         all_conv_peaks = utils.peak_finder(self.all_conv, WINDOW_SIZE * 2)
 
         filtered = self.__filter_prediction(all_conv_peaks, data)
-        return [(dataframe['timestamp'][x - 1].value, dataframe['timestamp'][x + 1].value) for x in filtered]
+        # TODO: convert from ns to ms more proper way (not dividing by 10^6)
+        return [(dataframe['timestamp'][x - 1].value / 1000000, dataframe['timestamp'][x + 1].value / 1000000) for x in filtered]
 
     def __filter_prediction(self, segments: list, data: list):
         if len(segments) == 0 or len(self.ipats) == 0:
diff --git a/analytics/models/jump_model.py b/analytics/models/jump_model.py
index 67a9929..e17d302 100644
--- a/analytics/models/jump_model.py
+++ b/analytics/models/jump_model.py
@@ -105,9 +105,10 @@ class JumpModel(Model):
     def do_predict(self, dataframe: pd.DataFrame):
         data = dataframe['value']
         possible_jumps = utils.find_jump(data, self.state['JUMP_HEIGHT'], self.state['JUMP_LENGTH'] + 1)
-        filtered = self.__filter_prediction(possible_jumps, data)
 
-        return [(dataframe['timestamp'][x - 1].value, dataframe['timestamp'][x + 1].value) for x in filtered]
+        filtered = self.__filter_prediction(possible_jumps, data)
+        # TODO: convert from ns to ms more proper way (not dividing by 10^6)
+        return [(dataframe['timestamp'][x - 1].value / 1000000, dataframe['timestamp'][x + 1].value / 1000000) for x in filtered]
 
     def __filter_prediction(self, segments, data):
         delete_list = []
diff --git a/analytics/models/peak_model.py b/analytics/models/peak_model.py
index 5b29cb7..799d86b 100644
--- a/analytics/models/peak_model.py
+++ b/analytics/models/peak_model.py
@@ -82,7 +82,8 @@ class PeakModel(Model):
                 segments.append(i)
 
         filtered = self.__filter_prediction(segments, data)
-        return [(dataframe['timestamp'][x - 1].value, dataframe['timestamp'][x + 1].value) for x in filtered]
+        # TODO: convert from ns to ms more proper way (not dividing by 10^6)
+        return [(dataframe['timestamp'][x - 1].value / 1000000, dataframe['timestamp'][x + 1].value / 1000000) for x in filtered]
 
     def __filter_prediction(self, segments: list, all_max_flatten_data: list):
         delete_list = []
diff --git a/analytics/models/trough_model.py b/analytics/models/trough_model.py
index 06ae826..f270edd 100644
--- a/analytics/models/trough_model.py
+++ b/analytics/models/trough_model.py
@@ -79,9 +79,10 @@ class TroughModel(Model):
         for i in all_mins:
             if all_max_flatten_data[i] < extrema_list[i]:
                 segments.append(i)
-        
+        test = dataframe['timestamp'][1].value
         filtered = self.__filter_prediction(segments, data)
-        return [(dataframe['timestamp'][x - 1].value, dataframe['timestamp'][x + 1].value) for x in filtered]
+        # TODO: convert from ns to ms more proper way (not dividing by 10^6)
+        return [(dataframe['timestamp'][x - 1].value / 1000000, dataframe['timestamp'][x + 1].value / 1000000) for x in filtered]
 
     def __filter_prediction(self, segments: list, all_max_flatten_data: list):
         delete_list = []