diff --git a/pytorch_forecasting/data/examples.py b/pytorch_forecasting/data/examples.py
index be89fa6f7..77443b776 100644
--- a/pytorch_forecasting/data/examples.py
+++ b/pytorch_forecasting/data/examples.py
@@ -50,6 +50,82 @@ def get_stallion_data() -> pd.DataFrame:
     return pd.read_parquet(fname)
 
 
+def get_stallion_dummy_data(seed: int | None = 0) -> pd.DataFrame:
+    """
+    Small dummy dataset for testing.
+
+    Returns:
+        pd.DataFrame: data
+    """
+    rng = np.random.default_rng(seed)
+    n_series = 350
+    dates = pd.date_range("2018-01-01", periods=24, freq="M")
+    agency_list = [f"Agency_{i:02d}" for i in range(20)]
+    sku_list = [f"SKU_{i:02d}" for i in range(25)]
+    pairs = [(a, s) for a in agency_list for s in sku_list]
+    selected_pairs = pairs[:n_series]
+    agencies = np.array([p[0] for p in selected_pairs])
+    skus = np.array([p[1] for p in selected_pairs])
+    n_rows = len(selected_pairs) * len(dates)
+
+    df = pd.DataFrame(
+        {
+            "agency": np.repeat(agencies, len(dates)),
+            "sku": np.repeat(skus, len(dates)),
+            "volume": rng.lognormal(7.0, 0.8, n_rows),
+            "date": np.tile(dates, len(selected_pairs)),
+            "industry_volume": np.clip(
+                rng.normal(5.4e8, 6.3e7, n_rows), 4.0e8, None
+            ).astype(np.int64),
+            "soda_volume": np.clip(
+                rng.normal(8.5e8, 8.0e7, n_rows), 6.5e8, None
+            ).astype(np.int64),
+            "avg_max_temp": rng.normal(28.5, 4.0, n_rows),
+            "price_regular": np.clip(rng.normal(1500.0, 450.0, n_rows), 100.0, 2000.0),
+            "discount": rng.gamma(2.0, 5.0, n_rows),
+            "avg_population_2017": np.clip(
+                rng.normal(60000, 8000, n_rows), 20000, None
+            ).astype(np.int64),
+            "avg_yearly_household_income_2017": np.clip(
+                rng.normal(35000, 5000, n_rows), 15000, None
+            ).astype(np.int64),
+            "timeseries": np.repeat(np.arange(len(selected_pairs)), len(dates)),
+        }
+    )
+
+    df["price_actual"] = np.maximum(df["price_regular"] - df["discount"], 0.0)
+    df["discount_in_percent"] = (
+        df["discount"] / np.maximum(df["price_regular"], 1.0)
+    ) * 100.0
+
+    holiday_cols = [
+        "easter_day",
+        "good_friday",
+        "new_year",
+        "christmas",
+        "labor_day",
+        "independence_day",
+        "revolution_day_memorial",
+        "regional_games",
+        "fifa_u_17_world_cup",
+        "football_gold_cup",
+        "beer_capital",
+        "music_fest",
+    ]
+    for col in holiday_cols:
+        df[col] = rng.binomial(1, 0.08, n_rows).astype(np.int64)
+
+    df["agency"] = df["agency"].astype("category")
+    df["sku"] = df["sku"].astype("category")
+    df["avg_max_temp"] = df["avg_max_temp"].astype(np.float64)
+    df["price_regular"] = df["price_regular"].astype(np.float64)
+    df["price_actual"] = df["price_actual"].astype(np.float64)
+    df["discount"] = df["discount"].astype(np.float64)
+    df = df.sort_values(["agency", "sku", "date"]).reset_index(drop=True)
+
+    return df
+
+
 def generate_ar_data(
     n_series: int = 10,
     timesteps: int = 400,
diff --git a/pytorch_forecasting/tests/_conftest.py b/pytorch_forecasting/tests/_conftest.py
index 8def0bfe2..def967ab9 100644
--- a/pytorch_forecasting/tests/_conftest.py
+++ b/pytorch_forecasting/tests/_conftest.py
@@ -4,7 +4,6 @@
 
 from pytorch_forecasting import TimeSeriesDataSet
 from pytorch_forecasting.data import EncoderNormalizer, GroupNormalizer, NaNLabelEncoder
-from pytorch_forecasting.data.examples import generate_ar_data, get_stallion_data
 
 torch.manual_seed(23)
 
diff --git a/pytorch_forecasting/tests/_data_scenarios.py b/pytorch_forecasting/tests/_data_scenarios.py
index c13ff0ae5..13ee4a8b1 100644
--- a/pytorch_forecasting/tests/_data_scenarios.py
+++ b/pytorch_forecasting/tests/_data_scenarios.py
@@ -6,14 +6,14 @@
 
 from pytorch_forecasting import TimeSeriesDataSet
 from pytorch_forecasting.data import EncoderNormalizer, GroupNormalizer, NaNLabelEncoder
-from pytorch_forecasting.data.examples import generate_ar_data, get_stallion_data
+from pytorch_forecasting.data.examples import generate_ar_data, get_stallion_dummy_data
 from pytorch_forecasting.data.timeseries import TimeSeries
 
 torch.manual_seed(23)
 
 
 def data_with_covariates():
-    data = get_stallion_data()
+    data = get_stallion_dummy_data()
     data["month"] = data.date.dt.month.astype(str)
     data["log_volume"] = np.log1p(data.volume)
     data["weight"] = 1 + np.sqrt(data.volume)
diff --git a/tests/conftest.py b/tests/conftest.py
index 608f06550..635955321 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -8,8 +8,7 @@
 
 
 from pytorch_forecasting import TimeSeriesDataSet  # isort:skip
-from pytorch_forecasting.data.examples import get_stallion_data  # isort:skip
-
+from pytorch_forecasting.data.examples import get_stallion_dummy_data  # isort:skip
 
 # for vscode debugging: https://stackoverflow.com/a/62563106/14121677
 if os.getenv("_PYTEST_RAISE", "0") != "0":
@@ -25,7 +24,7 @@ def pytest_internalerror(excinfo):
 
 @pytest.fixture(scope="session")
 def test_data():
-    data = get_stallion_data()
+    data = get_stallion_dummy_data()
     data["month"] = data.date.dt.month.astype(str)
     data["log_volume"] = np.log1p(data.volume)
     data["weight"] = 1 + np.sqrt(data.volume)