FORECAST¶

Syntax¶

CREATE [ OR REPLACE ] SNOWFLAKE.ML.FORECAST [ IF NOT EXISTS ] <model_name>(
    INPUT_DATA => <input_data>,
    [SERIES_COLNAME => '<series_colname>',]
    TIMESTAMP_COLNAME => '<timestamp_colname>',
    TARGET_COLNAME => '<target_colname>')
  [ [ WITH ] TAG ( <tag_name> = '<tag_value>' [ , <tag_name> = '<tag_value>' , ... ] ) ]
  [ COMMENT = '<string_literal>' ]

CREATE SNOWFLAKE.ML.FORECAST <name>(
  '<input_data>', '<series_colname>', '<timestamp_colname>', '<target_colname>');

Parameters¶

model_name

Specifies the identifier for the model; must be unique for the schema in which the model is created.

If the model identifier is not fully qualified (in the form of db_name.schema_name.name or schema_name.name), the command creates the model in the current schema for the session.

In addition, the identifier must start with an alphabetic character and cannot contain spaces or special characters unless the entire identifier string is enclosed in double quotes (for example, "My object"). Identifiers enclosed in double quotes are also case-sensitive.

For more details, see Identifier requirements.

Constructor Arguments¶

Required:

INPUT_DATA => input_data

A reference to the input data. Using a reference allows the training process, which runs with limited privileges, to use your privileges to access the data. You can use a reference to a table or a view if your data is already in that form, or you can use a query reference to provide the query to be executed to obtain the data.

The referenced data is the entire training data consumed by the forecasting model. If input_data contains any columns that are not named as timestamp_colname, target_colname, or series_colname, they are considered exogenous variables (additional features).

Order of the columns in the input data is irrelevant.

Your input data must have columns with appropriate types for your use case. See Examples for details on each use case.

Use Case	Columns and types
Single time series	Timestamp column: TIMESTAMP_NTZ Target value column: FLOAT
Multiple time series	Series column: VARIANT containing numeric values and text. Timestamp column: TIMESTAMP_NTZ Target value column: FLOAT
Single time series with exogenous variables	Timestamp column: TIMESTAMP_NTZ Target value column: FLOAT Exogenous feature columns: numeric or text
Multiple time series with exogenous variables	Series column: VARIANT containing numeric values and text. Timestamp column: TIMESTAMP_NTZ Target value column: FLOAT Exogenous feature columns: numeric or text

TIMESTAMP_COLNAME => 'timestamp_colname'

Name of the column containing the timestamps in input_data.

TARGET_COLNAME => 'target_colname'

Name of the column containing the training label (dependent value) in input_data.

Optional:

SERIES_COLNAME => 'series_colname'

For multiple time series models, the name of the column defining the multiple time series in input_data. This column can be a value of any type, or an array of values from one or more other columns, as shown in Forecast on Multiple Series.

If you are providing arguments positionally, this must be the second argument.

Usage Notes¶

Replication of class instances is currently not supported.

Examples¶

See Examples.

Syntax¶

SHOW SNOWFLAKE.ML.FORECAST [ LIKE <pattern> ]
                           [ IN
                               {
                                   ACCOUNT                  |

                                   DATABASE                 |
                                   DATABASE <database_name> |

                                   SCHEMA                   |
                                   SCHEMA <schema_name>     |
                                   <schema_name>
                                }
                            ]

Parameters¶

LIKE 'pattern'

Filters the command output by object name. The filter uses case-insensitive pattern matching with support for SQL wildcard characters (% and _).

For example, the following patterns return the same results:

... LIKE '%testing%' ...

... LIKE '%TESTING%' ...

[ IN ... ]

Optionally specifies the scope of the command. Specify one of the following:

ACCOUNT

Returns records for the entire account.

DATABASE, . DATABASE db_name

Returns records for the current database in use or for a specified database (db_name).

If you specify DATABASE without db_name and no database is in use, the keyword has no effect on the output.

SCHEMA, . SCHEMA schema_name, . schema_name

Returns records for the current schema in use or a specified schema (schema_name).

SCHEMA is optional if a database is in use or if you specify the fully qualified schema_name (for example, db.schema).

If no database is in use, specifying SCHEMA has no effect on the output.

Default: Depends on whether the session currently has a database in use:

• Database: DATABASE is the default (that is, the command returns the objects you have privileges to view in the database).

• No database: ACCOUNT is the default (that is, the command returns the objects you have privileges to view in your account).

Output¶

The command output provides model properties and metadata in the following columns:

Syntax¶

DROP SNOWFLAKE.ML.FORECAST [IF EXISTS] <name>;

Parameters¶

name

Specifies the identifier for the model to drop. If the identifier contains spaces, special characters, or mixed-case characters, the entire string must be enclosed in double quotes. Identifiers enclosed in double quotes are also case-sensitive.

If the model identifier is not fully qualified (in the form of db_name.schema_name.name or schema_name.name)), the command looks for the model in the current schema for the session.

Usage Notes¶

Dropped models cannot be recovered; they must be recreated.

Syntax¶

The required arguments vary depending on what use case the model was trained for.

For single-series models without exogenous variables:

<model_name>!FORECAST(
  FORECASTING_PERIODS => <forecasting_periods>,
  [CONFIG_OBJECT => <config_object>]
);

For single-series models with exogenous variables:

<model_name>!FORECAST(
  INPUT_DATA => <input_data>,
  TIMESTAMP_COLNAME => '<timestamp_colname>',
  [CONFIG_OBJECT => <config_object>]
);

For multiple-series models without exogenous variables:

<model_name>!FORECAST(
  SERIES_VALUE => <series>,
  FORECASTING_PERIODS => <forecasting_periods>,
  TIMESTAMP_COLNAME => '<timestamp_colname>',
  [CONFIG_OBJECT => <config_object>]
);

For multiple-series models with exogenous variables:

<model_name>!FORECAST(
  SERIES_VALUE => <series>,
  SERIES_COLNAME => <series_colname>,
  INPUT_DATA => <input_data>,
  TIMESTAMP_COLNAME => '<timestamp_colname>',
  [CONFIG_OBJECT => <config_object>]
);

Arguments¶

Required:

Not all of the following arguments are required for every use case previously listed.

FORECASTING_PERIODS => forecasting_periods

Required for forecasts without exogenous variables.

The number of steps ahead to forecast. The interval between steps is inferred by the model during training.

INPUT_DATA => input_data

Required for forecasts with exogenous variables. Your exogenous variables must be in the same order at this step as they were in the training step.

A reference to a table, view, or query that contains the future timestamps and values of the exogenous variables (additional user-provided features) that were passed as input_data when training the model. Using a reference allows the forecasting process, which runs with limited privileges, to use your privileges to access the data. Columns are matched between this argument and the original exogenous training data by name.

TIMESTAMP_COLNAME => 'timestamp_colname'

Required for forecasts with exogenous variables.

The name of the column in input_data containing the timestamps.

SERIES_COLNAME => 'series_colname'

Required for multi-series forecasts with exogenous variables.

The name of the column in input_data specifying the series.

SERIES_VALUE => series

Required for multi-series forecasts.

The time series to forecast. Can be a single value (e.g., ‘Series A’::variant) or an VARIANT but must specify a series that the model has been trained on. If not specified, all trained series are predicted.

Optional:

CONFIG_OBJECT => config_object

An OBJECT containing key-value pairs used to configure the forecast job. At present, there is only one configuration option.

Key	Type	Description
prediction_interval	FLOAT	[0, 1). Defaults to 0.95, meaning 95% of future points are expected to fall within the interval [lower_bound, upper_bound] from the forecast result.

Output¶

The SERIES column is present only for multi-series forecasts. Single-series forecasts do not have this column.

An additional column is also present for each exogenous variable. The number, names, and types of these vary, so they are not included here.

Syntax¶

<model_name>!EXPLAIN_FEATURE_IMPORTANCE();

Output¶