fix exogenous for auto; made default

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

@@ -57,14 +57,14 @@ EvaDB's default forecast framework is `statsforecast <https://nixtla.github.io/s
      - The name of column that represents an identifier for the series. If relevant column is not found, the whole table is considered as one series of data.
    * - LIBRARY (str, default: 'statsforecast')
      - We can select one of `statsforecast` (default) or `neuralforecast`. `statsforecast` provides access to statistical forecasting methods, while `neuralforecast` gives access to deep-learning based forecasting methods.
-   * - MODEL (str, default: 'AutoARIMA')
-     - If LIBRARY is `statsforecast`, we can select one of AutoARIMA, AutoCES, AutoETS, AutoTheta. The default is AutoARIMA. Check `Automatic Forecasting <https://nixtla.github.io/statsforecast/src/core/models_intro.html#automatic-forecasting>`_ to learn details about these models. If LIBRARY is `neuralforecast`, we can select one of NHITS or NBEATS. The default is NBEATS. Check `NBEATS docs <https://nixtla.github.io/neuralforecast/models.nbeats.html>`_ for details.
-   * - AUTO (str, default: 'F')
-     - The names of columns to be treated as exogenous variables, separated by comma. These columns would be considered for forecasting by the backend only for LIBRARY `neuralforecast`.
+   * - MODEL (str, default: 'ARIMA')
+     - If LIBRARY is `statsforecast`, we can select one of ARIMA, CES, ETS, Theta. The default is ARIMA. Check `Automatic Forecasting <https://nixtla.github.io/statsforecast/src/core/models_intro.html#automatic-forecasting>`_ to learn details about these models. If LIBRARY is `neuralforecast`, we can select one of NHITS or NBEATS. The default is NBEATS. Check `NBEATS docs <https://nixtla.github.io/neuralforecast/models.nbeats.html>`_ for details.
+   * - AUTO (str, default: 'T')
+     - If set to 'T', it enables automatic hyperparameter optimization. Must be set to 'T' for `statsforecast` library. One may set this parameter to `false` if LIBRARY is `neuralforecast` for faster (but less reliable) results.
    * - Frequency (str, default: 'auto')
      - A string indicating the frequency of the data. The common used ones are D, W, M, Y, which repestively represents day-, week-, month- and year- end frequency. The default value is M. Check `pandas available frequencies <https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases>`_ for all available frequencies. If it is not provided, the frequency is attempted to be determined automatically.
 
-Note: If columns other than the ones required as mentioned above are passed while creating the function, they will be treated as exogenous variables if LIBRARY is `neuralforecast` and the AUTO is set to F. In other situations, they would be ignored.
+Note: If columns other than the ones required as mentioned above are passed while creating the function, they will be treated as exogenous variables if LIBRARY is `neuralforecast`. Otherwise, they would be ignored.
 
 Below is an example query specifying the above parameters:
 
@@ -79,7 +79,7 @@ Below is an example query specifying the above parameters:
    ID 'type'
    Frequency 'W';
 
-Below is an example query with `neuralforecast` with `trend` column as exogenous:
+Below is an example query with `neuralforecast` with `trend` column as exogenous and without automatic hyperparameter optimization:
 
 .. code-block:: sql
    
@@ -89,4 +89,5 @@ Below is an example query with `neuralforecast` with `trend` column as exogenous
     HORIZON 12
     PREDICT 'y'
     LIBRARY 'neuralforecast'
+    AUTO 'f'
     FREQUENCY 'M';

evadb/executor/create_function_executor.py

@@ -290,9 +290,8 @@ def handle_forecasting_function(self):
             if "model" not in arg_map.keys():
                 arg_map["model"] = "NBEATS"
 
-            if (
-                "auto" in arg_map.keys()
-                and arg_map["auto"].lower()[0] == "t"
+            if "auto" not in arg_map.keys() or (
+                arg_map["auto"].lower()[0] == "t"
                 and "auto" not in arg_map["model"].lower()
             ):
                 arg_map["model"] = "Auto" + arg_map["model"]
@@ -307,15 +306,21 @@ def handle_forecasting_function(self):
 
             if "auto" not in arg_map["model"].lower():
                 model_args["input_size"] = 2 * horizon
-                if len(data.columns) >= 4:
-                    exogenous_columns = [
-                        x
-                        for x in list(data.columns)
-                        if x not in ["ds", "y", "unique_id"]
-                    ]
-                    model_args["hist_exog_list"] = exogenous_columns
-
                 model_args["early_stop_patience_steps"] = 20
+            else:
+                model_args["config"] = {
+                    "input_size": 2 * horizon,
+                    "early_stop_patience_steps": 20,
+                }
+
+            if len(data.columns) >= 4:
+                exogenous_columns = [
+                    x for x in list(data.columns) if x not in ["ds", "y", "unique_id"]
+                ]
+                if "auto" not in arg_map["model"].lower():
+                    model_args["hist_exog_list"] = exogenous_columns
+                else:
+                    model_args["config"]["hist_exog_list"] = exogenous_columns
 
             model_args["h"] = horizon
 
@@ -328,6 +333,10 @@ def handle_forecasting_function(self):
         #     Statsforecast implementation
         # """
         else:
+            if "auto" in arg_map.keys() and arg_map["auto"].lower()[0] != "t":
+                raise RuntimeError(
+                    "Statsforecast implementation only supports automatic hyperparameter optimization. Please set AUTO to true."
+                )
             try_to_import_statsforecast()
             from statsforecast import StatsForecast
             from statsforecast.models import AutoARIMA, AutoCES, AutoETS, AutoTheta
@@ -340,13 +349,9 @@ def handle_forecasting_function(self):
             }
 
             if "model" not in arg_map.keys():
-                arg_map["model"] = "AutoARIMA"
+                arg_map["model"] = "ARIMA"
 
-            if (
-                "auto" in arg_map.keys()
-                and arg_map["auto"].lower()[0] == "t"
-                and "auto" not in arg_map["model"].lower()
-            ):
+            if "auto" not in arg_map["model"].lower():
                 arg_map["model"] = "Auto" + arg_map["model"]
 
             try:
@@ -363,11 +368,7 @@ def handle_forecasting_function(self):
         data["ds"] = pd.to_datetime(data["ds"])
 
         model_save_dir_name = library + "_" + arg_map["model"] + "_" + new_freq
-        if (
-            len(data.columns) >= 4
-            and "auto" not in arg_map["model"].lower()
-            and library == "neuralforecast"
-        ):
+        if len(data.columns) >= 4 and library == "neuralforecast":
             model_save_dir_name += "_exogenous_" + str(sorted(exogenous_columns))
 
         model_dir = os.path.join(

test/integration_tests/long/test_model_forecasting.py

@@ -112,7 +112,7 @@ def test_forecast(self):
             SELECT AirPanelForecast() order by y;
         """
         result = execute_query_fetch_all(self.evadb, predict_query)
-        self.assertEqual(len(result), 12)
+        self.assertEqual(len(result), 24)
         self.assertEqual(
             result.columns,
             ["airpanelforecast.unique_id", "airpanelforecast.ds", "airpanelforecast.y"],