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univariate_time_series_forecast

Tutorial and use case​

Syntax​

univariate_time_series_forecast(<table_name>, <time_column_name>,
                                    <value_column_name>, <forecast_period>,
                                    <period_unit>)

Arguments​

<table_name> (string) - the data table's name.

<time_column_name> (string) - the name of a timestamp column in the data table.

<value_column_name> (string) - the name of a value column to be forecasted.

<forecast_period> (integer) - forecast period.

<period_unit> (string) - unit alias of the period.

Available <period_unit> aliases and descriptions:​

Alias stringDescription
'B'business day
'D'calendar day
'W'week
'M'month
'Q'quarter
'Y'year
info

Check out the Input data prerequisite for this here

Example​

SQL statement​

CALL univariate_time_series_forecast('SALES_TABLE', 'SALES_DATE', 'TOTAL_SALES', 24, 'M');

Description​

Based on historical SALES_DATE and TOTAL_SALES data in SALES_TABLE, forecast the TOTAL_SALES value for the next 24 M (months).

Result​

All historical and forecasted data is materialized as a new table.

A forecast dashboard is auto-generated for checking modeling accuracy, discovered trends, and predictions.

Prerequisite​

Input data format​

Input data is a univariate time series data: a sequence of integer or float data points indexed in timestamp. The table to be used should include in the following two columns:

  • A Time stamp column. Data type: Date & Time Data Types.
  • A value column. Data type: Fixed-point Number or Floating-point Number.
note

Extended reading. Snowflake docs about: Date & Time data types; Numeric data types.

Sample dataset: