diff --git a/requirements.txt b/requirements.txt
index ac71adc..8ba0951 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -2,6 +2,6 @@ fastapi==0.111.0
 pydantic==2.7.1
 uvicorn==0.29.0
 starlette~=0.37.2
-jaqpotpy==6.17.0
+jaqpotpy==6.18.2
 pre-commit==4.0.1
 ruff==0.6.3
diff --git a/src/helpers/predict_methods.py b/src/helpers/predict_methods.py
index 9a5f2d9..6f4658c 100644
--- a/src/helpers/predict_methods.py
+++ b/src/helpers/predict_methods.py
@@ -3,7 +3,14 @@
 from onnxruntime import InferenceSession
 from jaqpotpy.datasets import JaqpotpyDataset
 from src.helpers.recreate_preprocessor import recreate_preprocessor
-from jaqpotpy.doa import Leverage, BoundingBox, MeanVar
+from jaqpotpy.doa import (
+    Leverage,
+    BoundingBox,
+    MeanVar,
+    Mahalanobis,
+    KernelBased,
+    CityBlock,
+)
 
 
 def calculate_doas(input_feed, request):
@@ -18,7 +25,6 @@ def calculate_doas(input_feed, request):
               data instance. The keys in the dictionary are the names of the DoA methods used, and
               the values are the corresponding DoA predictions.
     """
-
     doas_results = []
     input_df = pd.DataFrame(input_feed["input"])
     for _, data_instance in input_df.iterrows():
@@ -34,6 +40,23 @@ def calculate_doas(input_feed, request):
             elif doa_data.method == "MEAN_VAR":
                 doa_method = MeanVar()
                 doa_method.bounds = doa_data.data["bounds"]
+            elif doa_data.method == "MAHALANOBIS":
+                doa_method = Mahalanobis()
+                doa_method._mean_vector = doa_data.data["meanVector"]
+                doa_method._inv_cov_matrix = doa_data.data["invCovMatrix"]
+                doa_method._threshold = doa_data.data["threshold"]
+            elif doa_data.method == "KERNEL_BASED":
+                doa_method = KernelBased()
+                doa_method._sigma = doa_data.data["sigma"]
+                doa_method._gamma = doa_data.data.get("gamma", None)
+                doa_method._threshold = doa_data.data["threshold"]
+                doa_method._kernel_type = doa_data.data["kernelType"]
+                doa_method._data = doa_data.data["dataPoints"]
+            elif doa_data.method == "CITY_BLOCK":
+                doa_method = CityBlock()
+                doa_method._mean_vector = doa_data.data["meanVector"]
+                doa_method._threshold = doa_data.data["threshold"]
+
             doa_instance_prediction[doa_method.__name__] = doa_method.predict(
                 pd.DataFrame(data_instance.values.reshape(1, -1))
             )[0]