diff --git a/experiments/vn1-competition/test_tabpfn.py b/experiments/vn1-competition/test_tabpfn.py
new file mode 100644
index 00000000..8daf95a6
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+++ b/experiments/vn1-competition/test_tabpfn.py
@@ -0,0 +1,124 @@
+from tabpfn_client import init
+from autogluon.timeseries import TimeSeriesDataFrame
+from tabpfn_time_series.data_preparation import generate_test_X
+
+from tabpfn_time_series import FeatureTransformer, DefaultFeatures
+from tabpfn_time_series import TabPFNTimeSeriesPredictor, TabPFNMode
+
+from time import time
+from pathlib import Path
+CURRENT_PATH = Path(__file__).parent
+
+import numpy as np
+import pandas as pd
+#%%
+predictor = TabPFNTimeSeriesPredictor(
+    tabpfn_mode=TabPFNMode.LOCAL,
+)
+
+selected_features = [
+    DefaultFeatures.add_running_index,
+    DefaultFeatures.add_calendar_features,
+]
+#%%
+def read_and_prepare_data(file_path: str, value_name: str = "y"):
+    """Reads data in wide format, and returns it in long format with columns `unique_id`, `ds`, `y`"""
+    df = pd.read_csv(file_path)
+    uid_cols = ["Client", "Warehouse", "Product"]
+    df["unique_id"] = df[uid_cols].astype(str).agg("-".join, axis=1)
+    df = df.drop(uid_cols, axis=1)
+    df = df.melt(id_vars=["unique_id"], var_name="ds", value_name=value_name)
+    df["ds"] = pd.to_datetime(df["ds"])
+    df = df.sort_values(by=["unique_id", "ds"])
+    return df
+#%%
+def get_train_data():
+    """Reads all train data and returns it in long format with columns `unique_id`, `ds`, `y`"""
+    train_list = [CURRENT_PATH.joinpath(f"data/phase_{i}_sales.csv") for i in [0, 1]]
+    train_list = [read_and_prepare_data(train_file) for train_file in train_list]
+    train_df = pd.concat(train_list).reset_index(drop=True)
+    train_df = train_df.sort_values(by=["unique_id", "ds"])
+
+    def remove_leading_zeros(group):
+        first_non_zero_index = group["y"].ne(0).idxmax()
+        return group.loc[first_non_zero_index:]
+
+    train_df = (
+        train_df.groupby("unique_id").apply(remove_leading_zeros).reset_index(drop=True)
+    )
+    train_df = train_df.rename(columns={
+        'unique_id': 'item_id',
+        'ds': 'timestamp',
+        'y': 'target'
+    })
+    train_df = train_df.set_index(['item_id', 'timestamp'])
+    train_tsdf = TimeSeriesDataFrame.from_data_frame(train_df)
+    return train_tsdf
+#%%
+def get_competition_forecasts() -> pd.DataFrame:
+    """Reads all competition forecasts and returns it in long format with columns `unique_id`, `ds`, `y`"""
+    fcst_df: pd.DataFrame | None = None
+    for place in ["1st", "2nd", "3rd", "4th", "5th"]:
+        fcst_df_place = read_and_prepare_data(
+            CURRENT_PATH.joinpath(f"data/solution_{place}_place.csv"), place
+        )
+        if fcst_df is None:
+            fcst_df = fcst_df_place
+        else:
+            fcst_df = fcst_df.merge(
+                fcst_df_place,
+                on=["unique_id", "ds"],
+                how="left",
+            )
+    return fcst_df
+#%%
+def vn1_competition_evaluation(forecasts: pd.DataFrame) -> pd.DataFrame:
+    """Computes competition evaluation scores"""
+    actual = read_and_prepare_data(CURRENT_PATH.joinpath("data/phase_2_sales.csv"))
+    res = actual[["unique_id", "ds", "y"]].merge(
+        forecasts, on=["unique_id", "ds"], how="left"
+    )
+    ids_forecasts = forecasts["unique_id"].unique()
+    ids_res = res["unique_id"].unique()
+    assert set(ids_forecasts) == set(ids_res), "Some unique_ids are missing"
+    scores = {}
+    for model in [col for col in forecasts.columns if col not in ["unique_id", "ds"]]:
+        abs_err = np.nansum(np.abs(res[model] - res["y"]))
+        err = np.nansum(res[model] - res["y"])
+        score = abs_err + abs(err)
+        score = score / res["y"].sum()
+        scores[model] = round(score, 4)
+    score_df = pd.DataFrame(list(scores.items()), columns=["model", "score"])
+    score_df = score_df.sort_values(by="score")
+    return score_df
+#%%
+def main():
+    """Complete pipeline"""
+    h=13
+    tabpfn_results = CURRENT_PATH.joinpath("tabpfn_preds.csv")
+    if not tabpfn_results.exists():
+        train_df = get_train_data()
+        test_df = generate_test_X(train_df, h)
+        train_df, test_df = FeatureTransformer.add_features(
+            train_df, test_df, selected_features
+        )
+        init = time()
+        fcst_df = predictor.predict(train_df, test_df)
+        print(f"TabPFN time: {time() - init}")
+        fcst_df = fcst_df.reset_index()
+        fcst_df = fcst_df[['item_id', 'timestamp', 'target']]
+        fcst_df = fcst_df.rename(columns={
+            'item_id': 'unique_id',
+            'timestamp': 'ds',
+            'target': 'tabpfn'
+        })
+        fcst_df.to_csv(CURRENT_PATH.joinpath("tabpfn_preds.csv"), index=False, header=True)
+    else:
+        fcst_df = pd.read_csv(tabpfn_results, parse_dates=['ds'])
+    fcst_df_comp = get_competition_forecasts()
+    fcst_df = fcst_df.merge(fcst_df_comp, on=["unique_id", "ds"], how="left")
+    eval_df = vn1_competition_evaluation(fcst_df)
+    print(eval_df)
+#%%
+if __name__ == '__main__':
+    main()
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