diff --git a/aeon/forecasting/_auto_naive.py b/aeon/forecasting/_auto_naive.py
new file mode 100644
index 0000000000..928fa530b2
--- /dev/null
+++ b/aeon/forecasting/_auto_naive.py
@@ -0,0 +1,86 @@
+"""Naive forecaster with parameters set on the training data."""
+
+"""Naive forecaster with multiple strategies."""
+
+__maintainer__ = ["TonyBagnall"]
+__all__ = ["AutoNaiveForecaster"]
+
+
+import numpy as np
+from enum import Enum
+from aeon.forecasting.base import BaseForecaster
+
+
+class AutoNaiveForecaster(BaseForecaster):
+    """
+    Naive forecaster with strategy set based on minimising error.
+
+    Searches options, "last", "mean", and "seasonal_last", with season in range [2,max_season].
+    If max_season is not passed to the constructor, it will be set to series length/2.
+
+    Simple first implementation, splits the train series into 70% train and 30% validation split
+    and minimises RMSE on the validation set.
+
+    Parameters
+    ----------
+    max_season : int or None, default=None
+        The maximum season to consider in the parameter search. In None, set as quarter the length of the series
+        passed in `fit`.
+
+    Examples
+    --------
+    >>> import aeon as ae
+    """
+
+    def __init__(self, max_season=None, horizon=1):
+        self.max_season = max_season
+        self.strategy_ = "last"
+        super().__init__(horizon=horizon, axis=1)
+
+    def _fit(self, y, exog=None):
+        y = y.squeeze()
+        self._y = y
+        # last strategy
+        mse_last = np.mean((y[1:] - y[:-1]) ** 2)
+
+        # series mean strategy, in sample
+        mse_mean = np.mean((y - np.mean(y)) ** 2)
+
+        # seasonal strategy, in sample
+        max_season = self.max_season
+        if self.max_season is None:
+            max_season = len(y)/4
+        best_s = None
+        best_seasonal = np.inf
+        for s in range(1, max_season + 1):
+            # Predict y[t] = y[t - s]
+            y_true = y[s:]
+            y_pred = y[:-s]
+            mse = np.mean((y_true - y_pred) ** 2)
+
+            if mse < best_seasonal:
+                best_seasonal = mse
+                best_s = s
+        self.best_mse_ = mse_last
+        self._fitted_scalar_value = y[:-1]
+
+        if mse_mean < mse_last:
+            self.strategy_ = "mean"
+            self.best_mse_ = mse_mean
+            self._fitted_scalar_value = np.mean(y)
+
+        if self.best_mse_ < best_seasonal:
+            self.strategy_ = "seasonal"
+            self.season_ = best_s
+            self.best_mse_ = best_seasonal
+
+        return self
+
+    def _predict(self, y, exog=None):
+        if self.strategy_ == "last" or self.strategy_ == "mean":
+                    return self._fitted_scalar_value
+            # For "seasonal_last" strategy
+        prediction_index = (self.horizon - 1) % self.season_
+        return self.self._y [prediction_index]
+
+
diff --git a/aeon/forecasting/_naive.py b/aeon/forecasting/_naive.py
index edb5b3ea62..ed800348f7 100644
--- a/aeon/forecasting/_naive.py
+++ b/aeon/forecasting/_naive.py
@@ -1,6 +1,6 @@
 """Naive forecaster with multiple strategies."""
 
-__maintainer__ = []
+__maintainer__ = ["TonyBagnall"]
 __all__ = ["NaiveForecaster"]