diff --git a/runtime/tests/automl_runtime/forecast/prophet/model_test.py b/runtime/tests/automl_runtime/forecast/prophet/model_test.py
index 1af65ea..4ce65da 100644
--- a/runtime/tests/automl_runtime/forecast/prophet/model_test.py
+++ b/runtime/tests/automl_runtime/forecast/prophet/model_test.py
@@ -37,7 +37,7 @@
 
 PROPHET_MODEL_JSON = '{"growth": "linear", "n_changepoints": 6, "specified_changepoints": false, "changepoint_range": 0.8, "yearly_seasonality": "auto", "weekly_seasonality": "auto", "daily_seasonality": "auto", "seasonality_mode": "additive", "seasonality_prior_scale": 10.0, "changepoint_prior_scale": 0.05, "holidays_prior_scale": 10.0, "mcmc_samples": 0, "interval_width": 0.8, "uncertainty_samples": 1000, "y_scale": 8.0, "logistic_floor": false, "country_holidays": null, "component_modes": {"additive": ["weekly", "additive_terms", "extra_regressors_additive", "holidays"], "multiplicative": ["multiplicative_terms", "extra_regressors_multiplicative"]}, "changepoints": "{\\"name\\":\\"ds\\",\\"index\\":[1,2,3,4,5,6],\\"data\\":[\\"2020-10-04T00:00:00.000\\",\\"2020-10-07T00:00:00.000\\",\\"2020-10-10T00:00:00.000\\",\\"2020-10-13T00:00:00.000\\",\\"2020-10-16T00:00:00.000\\",\\"2020-10-19T00:00:00.000\\"]}", "history_dates": "{\\"name\\":\\"ds\\",\\"index\\":[0,1,2,3,4,5,6,7,8],\\"data\\":[\\"2020-10-01T00:00:00.000\\",\\"2020-10-04T00:00:00.000\\",\\"2020-10-07T00:00:00.000\\",\\"2020-10-10T00:00:00.000\\",\\"2020-10-13T00:00:00.000\\",\\"2020-10-16T00:00:00.000\\",\\"2020-10-19T00:00:00.000\\",\\"2020-10-22T00:00:00.000\\",\\"2020-10-25T00:00:00.000\\"]}", "train_holiday_names": null, "start": 1601510400.0, "t_scale": 2073600.0, "holidays": null, "history": "{\\"schema\\":{\\"fields\\":[{\\"name\\":\\"ds\\",\\"type\\":\\"datetime\\"},{\\"name\\":\\"y\\",\\"type\\":\\"integer\\"},{\\"name\\":\\"floor\\",\\"type\\":\\"integer\\"},{\\"name\\":\\"t\\",\\"type\\":\\"number\\"},{\\"name\\":\\"y_scaled\\",\\"type\\":\\"number\\"}],\\"pandas_version\\":\\"1.4.0\\"},\\"data\\":[{\\"ds\\":\\"2020-10-01T00:00:00.000\\",\\"y\\":0,\\"floor\\":0,\\"t\\":0.0,\\"y_scaled\\":0.0},{\\"ds\\":\\"2020-10-04T00:00:00.000\\",\\"y\\":1,\\"floor\\":0,\\"t\\":0.125,\\"y_scaled\\":0.125},{\\"ds\\":\\"2020-10-07T00:00:00.000\\",\\"y\\":2,\\"floor\\":0,\\"t\\":0.25,\\"y_scaled\\":0.25},{\\"ds\\":\\"2020-10-10T00:00:00.000\\",\\"y\\":3,\\"floor\\":0,\\"t\\":0.375,\\"y_scaled\\":0.375},{\\"ds\\":\\"2020-10-13T00:00:00.000\\",\\"y\\":4,\\"floor\\":0,\\"t\\":0.5,\\"y_scaled\\":0.5},{\\"ds\\":\\"2020-10-16T00:00:00.000\\",\\"y\\":5,\\"floor\\":0,\\"t\\":0.625,\\"y_scaled\\":0.625},{\\"ds\\":\\"2020-10-19T00:00:00.000\\",\\"y\\":6,\\"floor\\":0,\\"t\\":0.75,\\"y_scaled\\":0.75},{\\"ds\\":\\"2020-10-22T00:00:00.000\\",\\"y\\":7,\\"floor\\":0,\\"t\\":0.875,\\"y_scaled\\":0.875},{\\"ds\\":\\"2020-10-25T00:00:00.000\\",\\"y\\":8,\\"floor\\":0,\\"t\\":1.0,\\"y_scaled\\":1.0}]}", "train_component_cols": "{\\"schema\\":{\\"fields\\":[{\\"name\\":\\"additive_terms\\",\\"type\\":\\"integer\\"},{\\"name\\":\\"weekly\\",\\"type\\":\\"integer\\"},{\\"name\\":\\"multiplicative_terms\\",\\"type\\":\\"integer\\"}],\\"pandas_version\\":\\"1.4.0\\"},\\"data\\":[{\\"additive_terms\\":1,\\"weekly\\":1,\\"multiplicative_terms\\":0},{\\"additive_terms\\":1,\\"weekly\\":1,\\"multiplicative_terms\\":0},{\\"additive_terms\\":1,\\"weekly\\":1,\\"multiplicative_terms\\":0},{\\"additive_terms\\":1,\\"weekly\\":1,\\"multiplicative_terms\\":0},{\\"additive_terms\\":1,\\"weekly\\":1,\\"multiplicative_terms\\":0},{\\"additive_terms\\":1,\\"weekly\\":1,\\"multiplicative_terms\\":0}]}", "changepoints_t": [0.125, 0.25, 0.375, 0.5, 0.625, 0.75], "seasonalities": [["weekly"], {"weekly": {"period": 7, "fourier_order": 3, "prior_scale": 10.0, "mode": "additive", "condition_name": null}}], "extra_regressors": [[], {}], "fit_kwargs": {}, "params": {"lp__": [[202.053]], "k": [[1.19777]], "m": [[0.0565623]], "delta": [[-0.86152, 0.409957, -0.103241, 0.528979, 0.535181, -0.509356]], "sigma_obs": [[2.53056e-13]], "beta": [[-0.00630566, 0.016248, 0.0318587, -0.068705, 0.0029986, -0.00410522]], "trend": [[0.0565623, 0.206283, 0.248314, 0.341589, 0.421959, 0.568452, 0.781842, 0.931562, 1.08128]]}, "__prophet_version": "1.1.1"}'
 
-class BaseTest(unittest.TestCase):
+class BaseProphetModelTest(unittest.TestCase):
     def _check_requirements(self, run_id: str):
         # read requirements.txt from the run
         requirements_path = mlflow.artifacts.download_artifacts(f"runs:/{run_id}/model/requirements.txt")
@@ -47,7 +47,7 @@ def _check_requirements(self, run_id: str):
         for dependency in PROPHET_ADDITIONAL_PIP_DEPS:
             self.assertIn(dependency, requirements, f"requirements.txt should contain {dependency} but got {requirements}")
 
-class TestProphetModel(BaseTest):
+class TestProphetModel(BaseProphetModelTest):
     @classmethod
     def setUpClass(cls) -> None:
         num_rows = 9
@@ -159,7 +159,7 @@ def test_validate_predict_cols(self):
         assert e.value.error_code == ErrorCode.Name(INTERNAL_ERROR)
 
 
-class TestMultiSeriesProphetModel(BaseTest):
+class TestMultiSeriesProphetModel(BaseProphetModelTest):
     @classmethod
     def setUpClass(cls) -> None:
         cls.model_json = PROPHET_MODEL_JSON