[ENH] NeuralForecastRNN should auto-detect freq (sktime#6003)

Enhances `NeuralForecastRNN` to interpret `freq` from `ForecastingHorizon` when passed as `"auto"`

.all-contributorsrc

@@ -2604,6 +2604,15 @@
         "bug",
         "code"
       ]
+    },
+    {
+      "login": "geetu040",
+      "name": "Armaghan",
+      "avatar_url": "https://avatars.githubusercontent.com/u/90601662?s=96&v=4",
+      "profile": "https://github.com/geetu040",
+      "contributions": [
+        "code"
+      ]
     }
   ]
 }

sktime/forecasting/base/adapters/_neuralforecast.py

@@ -17,8 +17,10 @@ class _NeuralForecastAdapter(BaseForecaster):
 
     Parameters
     ----------
-    freq : str
+    freq : str (default="auto")
         frequency of the data, see available frequencies [1]_ from ``pandas``
+
+        default ("auto") interprets freq from ForecastingHorizon in ``fit``
     local_scaler_type : str (default=None)
         scaler to apply per-series to all features before fitting, which is inverted
         after predicting
@@ -66,7 +68,7 @@ class _NeuralForecastAdapter(BaseForecaster):
 
     def __init__(
         self: "_NeuralForecastAdapter",
-        freq: str,
+        freq: str = "auto",
         local_scaler_type: typing.Optional[
             typing.Literal["standard", "robust", "robust-iqr", "minmax", "boxcox"]
         ] = None,
@@ -179,6 +181,16 @@ def _fit(
         if not fh.is_all_out_of_sample(cutoff=self.cutoff):
             raise NotImplementedError("in-sample prediction is currently not supported")
 
+        if self.freq == "auto":
+            if fh.freq:
+                # interpret freq from ForecastingHorizon
+                self.freq = fh.freq
+            else:
+                # when freq is not interpreted from ForecastingHorizon
+                raise ValueError(
+                    "Could not interpret freq, try passing freq in model initialization"
+                )
+
         train_indices = y.index
         if isinstance(train_indices, pandas.PeriodIndex):
             train_indices = train_indices.to_timestamp(freq=self.freq)

sktime/forecasting/neuralforecast.py

@@ -22,8 +22,10 @@ class NeuralForecastRNN(_NeuralForecastAdapter):
 
     Parameters
     ----------
-    freq : str
+    freq : str (default="auto")
         frequency of the data, see available frequencies [4]_ from ``pandas``
+
+        default ("auto") interprets freq from ForecastingHorizon in ``fit``
     local_scaler_type : str (default=None)
         scaler to apply per-series to all features before fitting, which is inverted
         after predicting
@@ -156,7 +158,7 @@ class NeuralForecastRNN(_NeuralForecastAdapter):
 
     def __init__(
         self: "NeuralForecastRNN",
-        freq: str,
+        freq: str = "auto",
         local_scaler_type: typing.Optional[
             typing.Literal["standard", "robust", "robust-iqr", "minmax", "boxcox"]
         ] = None,