diff --git a/tests/models/utils/test_forecaster.py b/tests/models/utils/test_forecaster.py
index f3875010..2b96d450 100644
--- a/tests/models/utils/test_forecaster.py
+++ b/tests/models/utils/test_forecaster.py
@@ -2,6 +2,7 @@
 import pytest
 from utilsforecast.data import generate_series
 
+from timecopilot.models.prophet import Prophet
 from timecopilot.models.stats import SeasonalNaive
 from timecopilot.models.utils.forecaster import (
     QuantileConverter,
@@ -332,3 +333,16 @@ def test_detect_anomalies_short_series_error():
     )
     with pytest.raises(ValueError, match="Cannot perform anomaly detection"):
         model.detect_anomalies(df, h=5, freq="D")
+
+
+def test_prophet_detect_anomalies_short_series_error():
+    model = Prophet()
+    df = pd.DataFrame(
+        {
+            "unique_id": ["A", "A"],
+            "ds": pd.date_range("2023-01-01", periods=2, freq="D"),
+            "y": [1.0, 2.0],
+        }
+    )
+    with pytest.raises(ValueError, match="Cannot perform anomaly detection"):
+        model.detect_anomalies(df, h=1, freq="D")
diff --git a/timecopilot/models/utils/forecaster.py b/timecopilot/models/utils/forecaster.py
index eb1b5df1..2201e49f 100644
--- a/timecopilot/models/utils/forecaster.py
+++ b/timecopilot/models/utils/forecaster.py
@@ -9,6 +9,7 @@
 from gluonts.time_feature.seasonality import (
     get_seasonality as _get_seasonality,
 )
+from prophet import Prophet as ProphetBase
 from scipy import stats
 from tqdm import tqdm
 from utilsforecast.plotting import plot_series
@@ -304,6 +305,7 @@ def detect_anomalies(
         Args:
             df (pd.DataFrame):
                 DataFrame containing the time series to detect anomalies.
+                Minimum series length is 1 higher for Prophet than other models.
             h (int, optional):
                 Forecast horizon specifying how many future steps to predict.
                 In each cross validation window. If not provided, the seasonality
@@ -348,14 +350,19 @@ def detect_anomalies(
         min_series_length = df.groupby("unique_id").size().min()
         # we require at least one observation before the first forecast
         max_possible_windows = (min_series_length - 1) // h
+        # 3 row minimum for a df with Prophet
+        if isinstance(self, ProphetBase):
+            max_possible_windows -= 1
         if n_windows is None:
             _n_windows = max_possible_windows
         else:
             _n_windows = min(n_windows, max_possible_windows)
         if _n_windows < 1:
+            # min series length should be 1 higher for Prophet than other models
+            exp_min_series_length = h + 2 if isinstance(self, ProphetBase) else h + 1
             raise ValueError(
                 f"Cannot perform anomaly detection: series too short. "
-                f"Minimum series length required: {h + 1}, "
+                f"Minimum series length required: {exp_min_series_length}, "
                 f"actual minimum length: {min_series_length}"
             )
         cv_results = self.cross_validation(