diff --git a/analytics/analytics/analytic_unit_worker.py b/analytics/analytics/analytic_unit_worker.py
index 3501334..decc4b1 100644
--- a/analytics/analytics/analytic_unit_worker.py
+++ b/analytics/analytics/analytic_unit_worker.py
@@ -57,7 +57,14 @@ class AnalyticUnitWorker:
           'lastDetectionTime': None
         }
 
-        for chunk in get_intersected_chunks(data, chunk_intersection, chunk_size):
+        chunks = []
+        # XXX: get_chunks(data, chunk_size) == get_intersected_chunks(data, 0, chunk_size)
+        if self._detector.is_detection_intersected():
+            chunks = get_intersected_chunks(data, chunk_intersection, chunk_size)
+        else:
+            chunks = get_chunks(data, chunk_size)
+
+        for chunk in chunks:
             await asyncio.sleep(0)
             chunk_dataframe = prepare_data(chunk)
             detected = self._detector.detect(chunk_dataframe, cache)
diff --git a/analytics/analytics/detectors/anomaly_detector.py b/analytics/analytics/detectors/anomaly_detector.py
index 06887c1..c248001 100644
--- a/analytics/analytics/detectors/anomaly_detector.py
+++ b/analytics/analytics/detectors/anomaly_detector.py
@@ -25,24 +25,28 @@ class AnomalyDetector(Detector):
 
     def detect(self, dataframe: pd.DataFrame, cache: Optional[ModelCache]) -> dict:
         data = dataframe['value']
-        last_values = None
+        alpha = cache['alpha']
+        confidence = cache['confidence']
+
+        last_value = None
         if cache is not None:
-            last_values = cache['last_values']
+            last_value = cache.get('lastValue')
 
-        #TODO detection code here
-        smoth_data = utils.exponential_smoothing(data, cache['alpha'])
-        upper_bound = utils.exponential_smoothing(data + cache['confidence'], cache['alpha'])
-        lower_bound = utils.exponential_smoothing(data - cache['confidence'], cache['alpha'])
+        smooth_data = utils.exponential_smoothing(data, alpha, last_value)
+        upper_bound = utils.exponential_smoothing(data + confidence, alpha, last_value)
+        lower_bound = utils.exponential_smoothing(data - confidence, alpha, last_value)
 
-        segemnts = []
+        segments = []
         for idx, val in enumerate(data.values):
             if val > upper_bound[idx] or val < lower_bound[idx]:
-                segemnts.append(idx)
+                segments.append(idx)
 
         last_detection_time = dataframe['timestamp'][-1]
+        cache['lastValue'] = smooth_data[-1]
+
         return {
             'cache': cache,
-            'segments': segemnts,
+            'segments': segments,
             'lastDetectionTime': last_detection_time
         }
 
@@ -76,3 +80,7 @@ class AnomalyDetector(Detector):
 
         #TODO: calculate value based on `alpha` value from cache
         return 1
+
+
+    def is_detection_intersected(self) -> bool:
+        return False
\ No newline at end of file
diff --git a/analytics/analytics/detectors/detector.py b/analytics/analytics/detectors/detector.py
index 6b44161..c0dc63e 100644
--- a/analytics/analytics/detectors/detector.py
+++ b/analytics/analytics/detectors/detector.py
@@ -24,3 +24,6 @@ class Detector(ABC):
     @abstractmethod
     def get_window_size(self, cache: Optional[ModelCache]) -> int:
         pass
+
+    def is_detection_intersected(self) -> bool:
+        return True