Add feedback for forecasting

docs/source/reference/ai/model-forecasting.rst

@@ -28,16 +28,18 @@ Next, we create a function of `TYPE Forecasting`. We must enter the column name
    CREATE FUNCTION IF NOT EXISTS Forecast FROM
    (SELECT y FROM AirData)
    TYPE Forecasting
+   HORIZON 12
    PREDICT 'y';
 
 This trains a forecasting model. The model can be called by providing the horizon for forecasting.
 
 .. code-block:: sql
 
-   SELECT Forecast(12);
+   SELECT Forecast();
 
-Here, the horizon is `12`, which represents the forecast 12 steps into the future.
+.. note::
 
+   `Forecasting` function also provides suggestions by default. If you wish to turn it off, send "False" as an optional argument while calling the function. Eg. `SELECT Forecast("False");`
 
 Forecast Parameters
 -------------------
@@ -90,4 +92,5 @@ Below is an example query with `neuralforecast` with `trend` column as exogenous
     PREDICT 'y'
     LIBRARY 'neuralforecast'
     AUTO 'f'
-    FREQUENCY 'M';
+    FREQUENCY 'M';
+

evadb/functions/forecast.py

@@ -48,13 +48,33 @@ def setup(
         self.id_column_rename = id_column_rename
         self.horizon = int(horizon)
         self.library = library
+        self.suggestion_dict = {
+            1: "Predictions are flat. Consider using LIBRARY 'neuralforecast' for more accrate predictions.",
+        }
 
     def forward(self, data) -> pd.DataFrame:
         if self.library == "statsforecast":
-            forecast_df = self.model.predict(h=self.horizon)
+            forecast_df = self.model.predict(h=self.horizon).reset_index()
         else:
-            forecast_df = self.model.predict()
-        forecast_df.reset_index(inplace=True)
+            forecast_df = self.model.predict().reset_index()
+
+        # Suggestions
+        if len(data) == 0 or list(data[0])[0].lower()[0] == "t":
+            suggestion_list = []
+            # 1: Flat predictions
+            if self.library == "statsforecast":
+                for type_here in forecast_df["unique_id"].unique():
+                    if (
+                        forecast_df.loc[forecast_df["unique_id"] == type_here][
+                            self.model_name
+                        ].nunique()
+                        == 1
+                    ):
+                        suggestion_list.append(1)
+
+            for suggestion in set(suggestion_list):
+                print("\nSUGGESTION: " + self.suggestion_dict[suggestion])
+
         forecast_df = forecast_df.rename(
             columns={
                 "unique_id": self.id_column_rename,