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diff --git a/csit.infra.dash/app/cdash/utils/anomalies.py b/csit.infra.dash/app/cdash/utils/anomalies.py
new file mode 100644
index 0000000000..9a7b232fda
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+++ b/csit.infra.dash/app/cdash/utils/anomalies.py
@@ -0,0 +1,69 @@
+# Copyright (c) 2023 Cisco and/or its affiliates.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at:
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Functions used by Dash applications to detect anomalies.
+"""
+
+from numpy import isnan
+
+from ..jumpavg import classify
+
+
+def classify_anomalies(data):
+    """Process the data and return anomalies and trending values.
+
+    Gather data into groups with average as trend value.
+    Decorate values within groups to be normal,
+    the first value of changed average as a regression, or a progression.
+
+    :param data: Full data set with unavailable samples replaced by nan.
+    :type data: OrderedDict
+    :returns: Classification and trend values
+    :rtype: 3-tuple, list of strings, list of floats and list of floats
+    """
+    # NaN means something went wrong.
+    # Use 0.0 to cause that being reported as a severe regression.
+    bare_data = [0.0 if isnan(sample) else sample for sample in data.values()]
+    # TODO: Make BitCountingGroupList a subclass of list again?
+    group_list = classify(bare_data).group_list
+    group_list.reverse()  # Just to use .pop() for FIFO.
+    classification = list()
+    avgs = list()
+    stdevs = list()
+    active_group = None
+    values_left = 0
+    avg = 0.0
+    stdv = 0.0
+    for sample in data.values():
+        if isnan(sample):
+            classification.append("outlier")
+            avgs.append(sample)
+            stdevs.append(sample)
+            continue
+        if values_left < 1 or active_group is None:
+            values_left = 0
+            while values_left < 1:  # Ignore empty groups (should not happen).
+                active_group = group_list.pop()
+                values_left = len(active_group.run_list)
+            avg = active_group.stats.avg
+            stdv = active_group.stats.stdev
+            classification.append(active_group.comment)
+            avgs.append(avg)
+            stdevs.append(stdv)
+            values_left -= 1
+            continue
+        classification.append("normal")
+        avgs.append(avg)
+        stdevs.append(stdv)
+        values_left -= 1
+    return classification, avgs, stdevs