Time Series Forecasting

You can train a forecasting model easily in EvaDB.

Note

Install statsforecast in your EvaDB virtual environment: pip install eva[forecasting].

First, we create a table to insert required data.

CREATE TABLE AirData (
 unique_id TEXT(30),
 ds TEXT(30),
 y INTEGER);

LOAD CSV 'data/forecasting/air-passengers.csv' INTO AirData;

Next, we create a function of TYPE Forecasting. We must enter the column name on which we wish to forecast using PREDICT.

CREATE FUNCTION IF NOT EXISTS Forecast FROM
(SELECT y FROM AirData)
TYPE Forecasting
PREDICT 'y';

This trains a forecasting model. The model can be called by providing the horizon for forecasting.

SELECT Forecast(12);

Here, the horizon is 12, which represents the forecast 12 steps into the future.

Forecast Parameters

EvaDB’s default forecast framework is statsforecast.

Available Parameters

PREDICT (str, required)	The name of the column we wish to forecast.
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 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, ting, ETS, Theta. The default is ARIMA. Check 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 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 respectively represents day-, week-, month- and year- end frequency. The default value is M. Check pandas available frequencies 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:

CREATE FUNCTION IF NOT EXISTS HomeRentalForecast FROM
(SELECT saledate, ma, type FROM HomeData)
TYPE Forecasting
HORIZON 12
PREDICT 'ma'
TIME 'saledate'
ID 'type'
Frequency 'W';

Below is an example query with neuralforecast with trend column as exogenous and without automatic hyperparameter optimization:

CREATE FUNCTION AirPanelForecast FROM
(SELECT unique_id, ds, y, trend FROM AirDataPanel)
TYPE Forecasting
HORIZON 12
PREDICT 'y'
LIBRARY 'neuralforecast'
AUTO 'f'
FREQUENCY 'M';