diff --git a/aeon/forecasting/_broadcast.py b/aeon/forecasting/_broadcast.py
new file mode 100644
index 0000000000..a2c081af1a
--- /dev/null
+++ b/aeon/forecasting/_broadcast.py
@@ -0,0 +1,59 @@
+import numpy as np
+
+from aeon.base._base import _clone_estimator
+from aeon.forecasting.base import BaseForecaster
+
+
+class BroadcastForecaster(BaseForecaster):
+    """A wrapper for multtivariate forecasting.
+
+    Applies a given univariate capable forecaster independently to each channel of a
+    multivariate series.
+
+    Parameters
+    ----------
+    forecaster : BaseForecaster
+        An aeon-compatible univariate forecaster instance.
+    """
+
+    _tags = {
+        "capability:univariate": True,
+        "capability:multivariate": True,
+        "capability:missing_values": False,
+        "capability:horizon": True,
+        "capability:exogenous": False,
+        "fit_is_empty": False,
+        "y_inner_type": "np.ndarray",
+    }
+
+    def __init__(self, forecaster: BaseForecaster):
+        self.forecaster = forecaster
+        self.forecasters_ = []
+        super().__init__(horizon=forecaster.horizon, axis=forecaster.axis)
+        # match exogenous capability to wrapped forecaster
+        self.set_tags(
+            **{"capability:exogenous": forecaster.get_tag("capability:exogenous")}
+        )
+
+    def _fit(self, y, exog=None):
+        """Fit one forecaster per channel."""
+        n_channels = y.shape[0]  # (n_channels, n_timepoints) after preprocessing
+        self.forecasters_ = []
+        self.forecast_ = np.zeros(n_channels)
+        for i in range(n_channels):
+            f = _clone_estimator(self.forecaster)
+            f.horizon = self.horizon
+            f.fit(y[i], exog)
+            self.forecasters_.append(f)
+            self.forecast_[i] = f.forecast_
+        if n_channels == 1:
+            self.forecast_f.forecast_[0]
+        return self
+
+    def _predict(self, y, exog=None):
+        """Predict one ahead for each channel independently."""
+        n_channels = y.shape[0]
+        preds = np.zeros(n_channels, dtype=float)
+        for c in range(n_channels):
+            preds[c] = self.forecasters_[c].predict(y[c], exog=None)
+        return preds