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 (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 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 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 for all available frequencies.

Below is an example query specifying the above parameters:

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