diff --git a/api/ops/tasks/detection/core/detectionTypes/lifetime.py b/api/ops/tasks/detection/core/detectionTypes/lifetime.py
index bc1b7d0..e1b1c66 100644
--- a/api/ops/tasks/detection/core/detectionTypes/lifetime.py
+++ b/api/ops/tasks/detection/core/detectionTypes/lifetime.py
@@ -32,11 +32,13 @@ def lifetimeDetect(df, granularity):
     """
     Method to perform anomaly detection on given dataframe
     """
-    today = dt.datetime.now()
+    today = dt.datetime.now().replace(minute=0, second=0, microsecond=0, tzinfo=None)
     df["ds"] = pd.to_datetime(df["ds"])
     df = df.sort_values("ds")
     df["ds"] = df["ds"].apply(lambda date: date.isoformat()[:19])
-    todayISO = today.replace(hour=0, minute=0, second=0, microsecond=0, tzinfo=None).isoformat()[:19]
+    if granularity == "day":
+        today = today.replace(hour=0)
+    todayISO = today.isoformat()[:19]
     df = df[df["ds"] < todayISO]
     maxVal = df.y.max()
     minVal = df.y.min()
diff --git a/api/ops/tasks/detection/core/detectionTypes/percentageChange.py b/api/ops/tasks/detection/core/detectionTypes/percentageChange.py
index 1cc2bb5..f1e2492 100644
--- a/api/ops/tasks/detection/core/detectionTypes/percentageChange.py
+++ b/api/ops/tasks/detection/core/detectionTypes/percentageChange.py
@@ -27,11 +27,13 @@ def percentChangeDetect(df, granularity, threshold):
     Method to perform anomaly detection on given dataframe using fbProphet
     """
     threshold = float(threshold)
-    today = dt.datetime.now()
+    today = dt.datetime.now().replace(minute=0, second=0, microsecond=0, tzinfo=None)
     df["ds"] = pd.to_datetime(df["ds"])
     df = df.sort_values("ds")
     df["ds"] = df["ds"].apply(lambda date: date.isoformat()[:19])
-    todayISO = today.replace(hour=0, minute=0, second=0, microsecond=0, tzinfo=None).isoformat()[:19]
+    if granularity == "day":
+        today = today.replace(hour=0)
+    todayISO = today.isoformat()[:19]
     df = df[df["ds"] < todayISO]
     df["percentageChange"] = 100 * (df.y - df.y.shift(1)) / df.y.shift(1)
     df["anomaly"] = (abs(df["percentageChange"]) > threshold) * 14 + 1
diff --git a/api/ops/tasks/detection/core/detectionTypes/prophet.py b/api/ops/tasks/detection/core/detectionTypes/prophet.py
index 57b01a0..95ca856 100644
--- a/api/ops/tasks/detection/core/detectionTypes/prophet.py
+++ b/api/ops/tasks/detection/core/detectionTypes/prophet.py
@@ -40,12 +40,12 @@ def prophetDetect(df, granularity, iterations=None):
     """
     Method to perform anomaly detection on given dataframe using fbProphet
     """
-    today = dt.datetime.now()
+    today = dt.datetime.now().replace(minute=0, second=0, microsecond=0, tzinfo=None)
     df["ds"] = pd.to_datetime(df["ds"])
     df["ds"] = df["ds"].apply(lambda date: date.isoformat()[:19])
-    todayISO = today.replace(
-        hour=0, minute=0, second=0, microsecond=0, tzinfo=None
-    ).isoformat()[:19]
+    if granularity == "day":
+        today = today.replace(hour=0)
+    todayISO = today.isoformat()[:19]
     df = df[df["ds"] < todayISO]
     lastActualRow = df[-1:]
     lastISO = df.iloc[-1]["ds"]
diff --git a/api/ops/tasks/detection/core/detectionTypes/valueThreshold.py b/api/ops/tasks/detection/core/detectionTypes/valueThreshold.py
index fed896e..97ef9b5 100644
--- a/api/ops/tasks/detection/core/detectionTypes/valueThreshold.py
+++ b/api/ops/tasks/detection/core/detectionTypes/valueThreshold.py
@@ -49,11 +49,13 @@ def valueThresholdDetect(df, granularity, operator, value1, value2):
         "between": '((df["y"] >= lowerVal) & (df["y"] <= upperVal)) * 14 + 1',
         "!between": '((df["y"] < lowerVal) | (df["y"] > upperVal)) * 14 + 1'
     }
-    today = dt.datetime.now()
+    today = dt.datetime.now().replace(minute=0, second=0, microsecond=0, tzinfo=None)
     df["ds"] = pd.to_datetime(df["ds"])
     df = df.sort_values("ds")
     df["ds"] = df["ds"].apply(lambda date: date.isoformat()[:19])
-    todayISO = today.replace(hour=0, minute=0, second=0, microsecond=0, tzinfo=None).isoformat()[:19]
+    if granularity == "day":
+        today = today.replace(hour=0)
+    todayISO = today.isoformat()[:19]
     df = df[df["ds"] < todayISO]
     df["anomaly"] = eval(operationDict[operator])
     anomalyLatest = checkLatestAnomaly(df, operationStrDict[operator])