FORECAST
Updated: 30 September 2010
Note: This documentation is for the SQL2008 (and later) version of this XLeratorDB function, it is not compatible with SQL Server 2005.
Click here for the SQL2005 version of the FORECAST function
Use the multi-input aggregate FORECAST to calculate a future value by using existing values. The predicted value is a y-value for a given x-value. The known values are existing x-values and y-values, and the new value is predicted using linear regression.Where
Syntax
SELECT [wctStatistics].[wct].[FORECAST] (
,<@new_x, float,>
,<@Known_y, float,>
,<@Known_x, float,>)
Arguments
@New_x
the new x-value for which you want FORECAST to calculate the y-value. @New_x is an expression of type float or of a type that can be implicitly converted to float.
@Known_y
the y-values to be used in the FORECAST calculation. @Known_y is an expression of type float or of a type that can be implicitly converted to float.
@Known_x
the x-values to be used in the FORECAST calculation. @Known_xis an expression of type float or of a type that can be implicitly converted to float.
Return Types
float
Remarks
· FORECAST is an AGGREGATE function and follows the same conventions as all other AGGREGATE functions in SQL Server.
Examples
In this example, we calculate the trend for a single set of x- and y-values with a single new x value
SELECT wct.FORECAST(12.5, y, x) as FORECAST
FROM (
SELECT 0.75, 1 UNION ALL
SELECT 2.5, 2 UNION ALL
SELECT 6.75, 3 UNION ALL
SELECT 10, 4
) n(x,y)
This produces the following result
FORECAST
----------------------
4.79116945107399
(1 row(s) affected)
In this example, we will populate some temporary table with some historical financial information and then calculate a forecast. First, create the table and put some data in it:
CREATE TABLE #c(
SYM NVARCHAR(5),
YE BIGINT,
REV FLOAT,
GPROF FLOAT,
OPINC FLOAT,
NETINC FLOAT
)
INSERT INTO #c VALUES('YHOO',2009,6460.32,3588.57,386.69,597.99)
INSERT INTO #c VALUES('YHOO',2008,72.5,4185.14,12.96,418.92)
INSERT INTO #c VALUES('YHOO',2007,6969.27,4130.52,695.41,639.16)
INSERT INTO #c VALUES('YHOO',2006,6425.68,3749.96,940.97,751.39)
INSERT INTO #c VALUES('YHOO',2005,5257.67,3161.47,1107.73,1896.23)
INSERT INTO #c VALUES('GOOG',2009,23650.56,14806.45,8312.19,6520.45)
INSERT INTO #c VALUES('GOOG',2008,21795.55,13174.04,5537.21,4226.86)
INSERT INTO #c VALUES('GOOG',2007,16593.99,9944.9,54.44,4203.72)
INSERT INTO #c VALUES('GOOG',2006,10604.92,6379.89,3550,3077.45)
INSERT INTO #c VALUES('GOOG',2005,6138.56,3561.47,2017.28,1465.4)
INSERT INTO #c VALUES('MSFT',2009,62484,509,24167,18760)
INSERT INTO #c VALUES('MSFT',2008,58437,46282,21225,14569)
INSERT INTO #c VALUES('MSFT',2007,60420,48822,22271,17681)
INSERT INTO #c VALUES('MSFT',2006,51122,40429,18438,14065)
INSERT INTO #c VALUES('MSFT',2005,44282,36632,16064,12599)
INSERT INTO #c VALUES('ORCL',2009,26820,21056,9062,6135)
INSERT INTO #c VALUES('ORCL',2008,23252,18458,8321,5593)
INSERT INTO #c VALUES('ORCL',2007,22430,17449,7844,5521)
INSERT INTO #c VALUES('ORCL',2006,17996,13805,5974,4274)
INSERT INTO #c VALUES('ORCL',2005,14380,11145,4736,3381)
INSERT INTO #c VALUES('SAP',2009,10672,6980,2588,1748)
INSERT INTO #c VALUES('SAP',2008,11575,7370,2701,1847)
INSERT INTO #c VALUES('SAP',2007,10256,6631,2698,1906)
INSERT INTO #c VALUES('SAP',2006,9393,6064,2578,1871)
INSERT INTO #c VALUES('SAP',2005,8509,5460,2337,1496)
In this example, we will forecast the revenue (REV) against the year (YE)
SELECT SYM
,ROUND(wct.FORECAST(2010,REV,YE), 0) as [2010 Revenue]
,ROUND(wct.FORECAST(2011,REV,YE), 0) as [2011 Revenue]
,ROUND(wct.FORECAST(2012,REV,YE), 0) as [2012 Revenue]
FROM #c
GROUP BY SYM
This produces the following result.
SYM 2010 Revenue 2011 Revenue 2012 Revenue
----- ---------------------- ---------------------- ----------------------
GOOG 29621 34243 38864
MSFT 68465 72837 772
ORCL 30016 33030 36044
SAP 12033 12684 13335
YHOO 3856 3458 3063
(5 row(s) affected)
To calculate the net income using the revenue projections from the above query, we could enter the following statement.
SELECT #c.SYM
,ROUND(wct.FORECAST([2010 Revenue],NETINC, REV), 0) as [2010 Net Income]
,ROUND(wct.FORECAST([2011 Revenue],NETINC, REV), 0) as [2011 Net Income]
,ROUND(wct.FORECAST([2012 Revenue],NETINC, REV), 0) as [2012 Net Income]
FROM (
SELECT SYM
,ROUND(wct.FORECAST(2010,REV,YE), 0) as [2010 Revenue]
,ROUND(wct.FORECAST(2011,REV,YE), 0) as [2011 Revenue]
,ROUND(wct.FORECAST(2012,REV,YE), 0) as [2012 Revenue]
FROM #c
GROUP BY SYM) n, #c
WHERE n.sym = #c.sym
GROUP BY #c.SYM
This returns the following results.
SYM 2010 Net Income 2011 Net Income 2012 Net Income
----- ---------------------- ---------------------- ----------------------
GOOG 7107 8176 9246
MSFT 19566 20910 22253
ORCL 7060 7753 8447
SAP 1945 2002 2059
YHOO 807 789 771
(5 row(s) affected)
As the following query demonstrates, this returns a different result than if we had just looked at the net income over time.
SELECT SYM
,ROUND(wct.FORECAST(2010,NETINC, YE), 0) as [2010 Net Income]
,ROUND(wct.FORECAST(2011,NETINC, YE), 0) as [2011 Net Income]
,ROUND(wct.FORECAST(2012,NETINC, YE), 0) as [2012 Net Income]
FROM #c
GROUP BY SYM
This produces the following result.
SYM 2010 Net Income 2011 Net Income 2012 Net Income
----- ---------------------- ---------------------- ----------------------
GOOG 7277 8403 9529
MSFT 19383 20665 21948
ORCL 7029 7712 8394
SAP 1918 1966 2014
YHOO -18 -311 -604
(5 row(s) affected)
See Also