Add support for Neuralforecast (#1115)

Adding support for `neuralforecast`. Fixes #1112. ```sql DROP TABLE IF EXISTS AirData; CREATE TABLE AirData ( unique_id TEXT(30), ds TEXT(30), y INTEGER); LOAD CSV 'data/forecasting/air-passengers.csv' INTO AirData; DROP FUNCTION IF EXISTS Forecast; CREATE FUNCTION Forecast FROM (SELECT unique_id, ds, y FROM AirData) TYPE Forecasting PREDICT 'y' HORIZON 12 LIBRARY 'neuralforecast'; SELECT Forecast(12); ``` One quick issue here is that `neuralforecast` needs `horizon` as a parameter while training, unlike `statsforecast`. Thus, a better way to call the UDF would be simply `SELECT Forecast();`, which is currently unsupported. @xzdandy Please let me know your thoughts. List of stuff yet to be done: - [x] Incorporate `neuralforecast` - [x] Fix `HORIZON` redundancy (UPDATE: Being fixed in #1121) - [x] Reuse model with lower horizon no - [x] Add support for ~multivariate forecasting~ exogenous variables - [x] Add tests - [x] Add docs --------- Co-authored-by: xzdandy <xzdandy@gmail.com>
georgia-tech-db · Sep 30, 2023 · e8a181c · e8a181c
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diff --git a/data/forecasting/AirPassengersPanel.csv b/data/forecasting/AirPassengersPanel.csv
@@ -0,0 +1,289 @@
+ds,unique_id,y,trend,ylagged
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diff --git a/docs/source/reference/ai/model-forecasting.rst b/docs/source/reference/ai/model-forecasting.rst
@@ -47,16 +47,24 @@ EvaDB's default forecast framework is `statsforecast <https://nixtla.github.io/s
 .. list-table:: Available Parameters
    :widths: 25 75
 
-   * - PREDICT (**required**) 
+   * - PREDICT (str, required) 
      - The name of the column we wish to forecast.
-   * - TIME
-     - The name of the column that contains the datestamp, wihch should be of a format expected by Pandas, ideally YYYY-MM-DD for a date or YYYY-MM-DD HH:MM:SS for a timestamp. Please visit the `pandas documentation <https://pandas.pydata.org/docs/reference/api/pandas.to_datetime.html>`_ for details. If not provided, an auto increasing ID column will be used.
-   * - ID
-     - The name of column that represents an identifier for the series. If not provided, the whole table is considered as one series of data.
-   * - MODEL
-     - 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.
-   * - Frequency
-     - 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.
+   * - HORIZON (int, required) 
+     - The number of steps into the future we wish to forecast.
+   * - TIME (str, default: 'ds')
+     - The name of the column that contains the datestamp, which should be of a format expected by Pandas, ideally YYYY-MM-DD for a date or YYYY-MM-DD HH:MM:SS for a timestamp. Please visit the `pandas documentation <https://pandas.pydata.org/docs/reference/api/pandas.to_datetime.html>`_ for details. If relevant column is not found, an auto increasing ID column will be used.
+   * - ID (str, default: 'unique_id')
+     - 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: '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`. Otherwise, they would be ignored.
 
 Below is an example query specifying the above parameters:
 
@@ -65,8 +73,21 @@ Below is an example query specifying the above parameters:
    CREATE FUNCTION IF NOT EXISTS HomeRentalForecast FROM
    (SELECT saledate, ma, type FROM HomeData)
    TYPE Forecasting
+   HORIZON 12
    PREDICT 'ma'
    TIME 'saledate'
    ID 'type'
-   MODEL 'AutoCES'
    Frequency 'W';
+
+Below is an example query with `neuralforecast` with `trend` column as exogenous and without automatic hyperparameter optimization:
+
+.. code-block:: sql
+   
+    CREATE FUNCTION AirPanelForecast FROM
+    (SELECT unique_id, ds, y, trend FROM AirDataPanel)
+    TYPE Forecasting
+    HORIZON 12
+    PREDICT 'y'
+    LIBRARY 'neuralforecast'
+    AUTO 'f'
+    FREQUENCY 'M';
diff --git a/evadb/binder/statement_binder.py b/evadb/binder/statement_binder.py
@@ -126,10 +126,6 @@ def _bind_create_function_statement(self, node: CreateFunctionStatement):
                     elif column.name == arg_map.get("predict", "y"):
                         outputs.append(column)
                         required_columns.remove(column.name)
-                    else:
-                        raise BinderError(
-                            f"Unexpected column {column.name} found for forecasting function."
-                        )
                 assert (
                     len(required_columns) == 0
                 ), f"Missing required {required_columns} columns for forecasting function."