FORECAST - Adding a Trend Line

FORECAST functions returns linear regression estimates for up to 10 future periods based on existing data. Second parameter is the number of periods; the default is 3.

Use the FORECAST function to add a trend line to a visualization. A trend line uses linear regression to predict future periods based on the existing data. For example, you could use a trend line to predict future sales based on your historical data. Forecast visualizations must have one date/time attribute on the X axis, and no other attributes.

Syntax

SELECT FORECAST(... [, number of periods])

Optionally, you can specify a number of periods to forecast.

Format: number

Default: 3

All visualization filters are applied to the metric that you FORECAST except for date and time filters. To apply a date or time filter to the visualization, add it directly to the metric. For example:

SELECT FORECAST((SELECT COUNT({attribute/activity}) WHERE {label/date.year} = "2012"), 6)

Examples

SELECT FORECAST(SUM({fact/quantity}) , 6)

Suppose you create a forecast for the totals of Deal Size fact sliced by the Month (Sale Closed) attribute. Use the following metric to compute the next three months based on all historical data:

SELECT FORECAST(SUM({metric/deal_size}))

The following metric predicts Deal Size for the next six months:

SELECT FORECAST(SUM({metric/deal_size}), 6)

The following metric predicts Deal Size for the next two months using data from the previous year:

SELECT FORECAST((SELECT SUM({metric/deal_size}) WHERE {label/date.year} = PREVIOUS), 2)

The following metric predicts Deal Size for the next two months using data from the previous and current years:

SELECT FORECAST((SELECT SUM({metric/deal_size}) WHERE {label/date.year} = PREVIOUS OR {label/date.year} = THIS), 2)

The following metric predicts Deal Size for the next four months using data from the last twelve months:

SELECT FORECAST((SELECT SUM({metric/deal_size}) WHERE {label/date.month_year} >= THIS-12), 4)