Topics: |

You can calculate trends in numeric data and predict values beyond the range of those stored in the data source by using the FORECAST feature. FORECAST can be used in a report or graph request.

The calculations you can make to identify trends and forecast values are:

- Simple moving average (MOVAVE). Calculates a series of arithmetic means using a specified number of values from a field. For details, see Using a Simple Moving Average.
- Exponential moving average. Calculates
a weighted average between the previously calculated value of the
average and the next data point. There are three methods for using
an exponential moving average:
- Single exponential smoothing (EXPAVE). Calculates an average that allows you to choose weights to apply to newer and older values. For details, see Using Single Exponential Smoothing.
- Double exponential smoothing (DOUBLEXP). Accounts for the tendency of data to either increase or decrease over time without repeating. For details, see Using Double Exponential Smoothing.
- Triple exponential smoothing (SEASONAL). Accounts for the tendency of data to repeat itself in intervals over time. For details, see Using Triple Exponential Smoothing.

- Linear regression analysis (REGRESS). Derives the coefficients of a straight line that best fits the data points and uses this linear equation to estimate values. For details, see Usage Notes for Creating Virtual Fields.

When predicting values in addition to calculating trends, FORECAST continues the same calculations beyond the data points by using the generated trend values as new data points. For the linear regression technique, the calculated regression equation is used to derive trend and predicted values.

FORECAST performs the calculations based on the data provided, but decisions about their use and reliability are the responsibility of the user. Therefore, FORECAST predictions are not always reliable, and many factors determine how accurate a prediction will be.

How to: |

Reference: |

You invoke FORECAST processing by including FORECAST in a RECAP command. In this command, you specify the parameters needed for generating estimated values, including the field to use in the calculations, the type of calculation to use, and the number of predictions to generate. The RECAP field that contains the result of FORECAST can be a new field (non-recursive) or the same field used in the FORECAST calculations (recursive):

- In a recursive FORECAST, the RECAP field that contains the results is also the field used to generate the FORECAST calculations. In this case, the original field is not printed even if it was referenced in the display command, and the RECAP column contains the original field values followed by the number of predicted values specified in the FORECAST syntax. No trend values display in the report. However, the original column is stored in an output file unless you set HOLDLIST to PRINTONLY.
- In a non-recursive FORECAST, a new field contains the results of FORECAST calculations. The new field is displayed in the report along with the original field when it is referenced in the display command. The new field contains trend values and forecast values when specified.

FORECAST operates on the last ACROSS field in the request. If the request does not contain an ACROSS field, it operates on the last BY field. The FORECAST calculations start over when the highest-level sort field changes its value. In a request with multiple display commands, FORECAST operates on the last ACROSS field (or if there are no ACROSS fields, the last BY field) of the last display command. When using an ACROSS field with FORECAST, the display command must be SUM or COUNT.

Note: Although you pass parameters to FORECAST using an argument list in parentheses, FORECAST is not a function. It can coexist with a function of the same name, as long as the function is not specified in a RECAP command.

MOVAVE calculation

ONsortfieldRECAPresult_field[/fmt] = FORECAST(infield,interval,npredict, 'MOVAVE',npoint1)sendstyle

EXPAVE calculation

ONsortfieldRECAPresult_field[/fmt] = FORECAST(infield,interval,npredict, 'EXPAVE',npoint1);

DOUBLEXP calculation

ONsortfieldRECAPfld1[/fmt] = FORECAST(infield, interval, npredict, 'DOUBLEXP',npoint1, npoint2);

SEASONAL calculation

ONsortfieldRECAPfld1[/fmt] = FORECAST(infield, interval, npredict, 'SEASONAL',nperiod, npoint1, npoint2, npoint3);

REGRESS calculation

ONsortfieldRECAPresult_field[/fmt] = FORECAST(infield,interval,npredict, 'REGRESS');

where:

`sortfield`- Is the last ACROSS field in the request. This field must be in numeric or date format. If the request does not contain an ACROSS field, FORECAST works on the last BY field.
`result_field`- Is the field containing the result of FORECAST. It can be a
new field, or the same as
*infield*. This must be a numeric field; either a real field, a virtual field, or a calculated field.Note: The word FORECAST and the opening parenthesis must be on the same line as the syntax

*sortfield*=. `fmt`- Is the display format for
*result_field*. The default format is D12.2. If*result_field*was previously reformatted using a DEFINE or COMPUTE command, the format specified in the RECAP command is respected. `infield`- Is any numeric field. It can be the same field as
*result_field*, or a different field. It cannot be a date-time field or a numeric field with date display options. `interval`- Is the increment to add to each
*sortfield*value (after the last data point) to create the next value. This must be a positive integer. To sort in descending order, use the BY HIGHEST phrase. The result of adding this number to the*sortfield*values is converted to the same format as*sortfield*.For date fields, the minimal component in the format determines how the number is interpreted. For example, if the format is YMD, MDY, or DMY, an interval value of 2 is interpreted as meaning two days; if the format is YM, the 2 is interpreted as meaning two months.

`npredict`- Is the number of predictions for FORECAST to calculate. It must
be an integer greater than or equal to zero. Zero indicates that
you do not want predictions, and is only supported with a non-recursive
FORECAST. For the SEASONAL method, npredict is the number of
*periods*to calculate. The number of*points*generated is:`nperiod`*`npredict` `nperiod`- For the SEASONAL method, is a positive whole number that specifies the number of data points in a period.
`npoint1`- Is the number of values to average for the MOVAVE method. For
EXPAVE, DOUBLEXP, and SEASONAL, this number is used to calculate
the weights for each component in the average. This value must be
a positive whole number. The weight, k, is calculated by the following
formula:
k=2/(1+

`npoint1`) `npoint2`- For DOUBLEXP and SEASONAL, this positive whole number is used
to calculate the weights for each term in the trend. The weight,
g, is calculated by the following formula:
g=2/(1+

`npoint2`) `npoint3`- For SEASONAL, this positive whole number is used to calculate
the weights for each term in the seasonal adjustment. The weight,
p, is calculated by the following formula:
p=2/(1+

`npoint3`)

- The sort field used for FORECAST must be in a numeric or date format.
- When using simple moving average and exponential moving average methods, data values should be spaced evenly in order to get meaningful results.
- When using a RECAP command with FORECAST,
the command can
contain only the FORECAST syntax. FORECAST does
not recognize any syntax after the closing semicolon (;). To specify
options such as AS or IN:
- In a non-recursive FORECAST request, use an empty COMPUTE command prior to the RECAP.
- In a recursive FORECAST request, specify the options when the field is first referenced in the report request.

- The use of column notation is not supported in a request that includes FORECAST. The process of generating the FORECAST values creates extra columns that are not printed in the report output. The number and placement of these additional columns varies depending on the specific request.
- A request can contain up to seven non-FORECAST RECAP commands and up to seven additional FORECAST RECAP commands.
- The left side of a RECAP command used for FORECAST supports the CURR attribute for creating a currency-denominated field.
- Recursive and non-recursive REGRESS are not supported in the same request when the display command is SUM, ADD, or WRITE.
- Missing values are not supported with REGRESS.
- If you use the ESTRECORDS parameter to enable the external sort to estimate better the amount of sort work space needed, you must take into account that FORECAST with predictions creates additional records in the output.
- In a styled report,
you can assign specific attributes to values predicted by FORECAST with
the StyleSheet attribute WHEN=FORECAST. For example, to make the
predicted values display with the color red, use the following syntax
in the TABLE request:
ON TABLE SET STYLE *TYPE=DATA,COLUMN=MYFORECASTSORTFIELD,WHEN=FORECAST,COLOR=RED, $ENDSTYLE

The following are not supported with a RECAP command that uses FORECAST:

- BY TOTAL command.
- MORE, MATCH, FOR, and OVER phrases.
- SUMMARIZE and RECOMPUTE are not supported for the same sort field used for FORECAST.
- MISSING attribute.

A simple moving average is a series of arithmetic means calculated with a specified number of values from a field. Each new mean in the series is calculated by dropping the first value used in the prior calculation, and adding the next data value to the calculation.

Simple moving averages are sometimes used to analyze trends in stock prices over time. In this scenario, the average is calculated using a specified number of periods of stock prices. A disadvantage to this indicator is that because it drops the oldest values from the calculation as it moves on, it loses its memory over time. Also, mean values are distorted by extreme highs and lows, since this method gives equal weight to each point.

Predicted values beyond the range of the data values are calculated using a moving average that treats the calculated trend values as new data points.

The first complete moving average occurs at the n^{th} data
point because the calculation requires *n* values. This is
called the lag. The moving average values for the lag rows are calculated
as follows: the first value in the moving average column is equal
to the first data value, the second value in the moving average
column is the average of the first two data values, and so on until
the n^{th} row, at which point there are enough values to
calculate the moving average with the number of values specified.

This request defines an integer value named PERIOD to use as the independent variable for the moving average. It predicts three periods of values beyond the range of the retrieved data.

DEFINE FILE GGSALES SDATE/YYM = DATE; SYEAR/Y = SDATE; SMONTH/M = SDATE; PERIOD/I2 = SMONTH; END TABLE FILE GGSALES SUM UNITS DOLLARS BY CATEGORY BY PERIOD WHERE SYEAR EQ 97 AND CATEGORY NE 'Gifts' ON PERIOD RECAP MOVAVE/D10.1= FORECAST(DOLLARS,1,3,'MOVAVE',3); END

The output is:

In the report, the number of values to use in the average is 3 and there are no UNITS or DOLLARS values for the generated PERIOD values.

Each average (MOVAVE value) is computed using DOLLARS values where they exist. The calculation of the moving average begins in the following way:

- The first MOVAVE value (801,123.0) is equal to the first DOLLARS value.
- The second MOVAVE value (741,731.5) is the mean of DOLLARS values one and two: (801,123 + 682,340) /2.
- The third MOVAVE value (749,513.7) is the mean of DOLLARS values one through three: (801,123 + 682,340 + 765,078) / 3.
- The fourth MOVAVE value (712,897.3) is the mean of DOLLARS values two through four: (682,340 + 765,078 + 691,274) /3.

For predicted values beyond the supplied values, the calculated MOVAVE values are used as new data points to continue the moving average. The predicted MOVAVE values (starting with 694,975.6 for PERIOD 13) are calculated using the previous MOVAVE values as new data points. For example, the first predicted value (694,975.6) is the average of the data points from periods 11 and 12 (620,264 and 762,328) and the moving average for period 12 (702,334.7). The calculation is: 694,975 = (620,264 + 762,328 + 702,334.7)/3.

This request defines an integer value named PERIOD to use as the independent variable for the moving average. It predicts three periods of values beyond the range of the retrieved data. It uses the same name for the RECAP field as the first argument in the FORECAST parameter list. The trend values do not display in the report. The actual data values for DOLLARS are followed by the predicted values in the report column.

DEFINE FILE GGSALES SDATE/YYM = DATE; SYEAR/Y = SDATE; SMONTH/M = SDATE; PERIOD/I2 = SMONTH; END TABLE FILE GGSALES SUM UNITS DOLLARS BY CATEGORY BY PERIOD WHERE SYEAR EQ 97 AND CATEGORY NE 'Gifts' ON PERIOD RECAP DOLLARS/D10.1 = FORECAST(DOLLARS,1,3,'MOVAVE',3); END

The output is:

The single exponential smoothing method calculates an average that allows you to choose weights to apply to newer and older values.

The following formula determines the weight given to the newest value.

k= 2/(1+n)

where:

`k`- Is the newest value.
`n`- Is an integer greater than one. Increasing
*n*increases the weight assigned to the earlier observations (or data instances), as compared to the later ones.

The next calculation of the exponential moving average (EMA) value is derived by the following formula:

EMA = (EMA * (1-k)) + (datavalue *k)

This means that the newest value from the data source is multiplied
by the factor *k* and the current moving average is multiplied
by the factor (1-*k*). These quantities are then summed to
generate the new EMA.

Note: When the data values are exhausted, the last data value in the sort group is used as the next data value.

The following defines an integer value named PERIOD to use as the independent variable for the moving average. It predicts three periods of values beyond the range of retrieved data.

DEFINE FILE GGSALES SDATE/YYM = DATE; SYEAR/Y = SDATE; SMONTH/M = SDATE; PERIOD/I2 = SMONTH; END TABLE FILE GGSALES SUM UNITS DOLLARS BY CATEGORY BY PERIOD WHERE SYEAR EQ 97 AND CATEGORY NE 'Gifts' ON PERIOD RECAP EXPAVE/D10.1= FORECAST(DOLLARS,1,3,'EXPAVE',3); END

The output is:

In the report, three predicted values of EXPAVE are calculated within each value of CATEGORY. For values outside the range of the data, new PERIOD values are generated by adding the interval value (1) to the prior PERIOD value.

Each average (EXPAVE value) is computed using DOLLARS values where they exist. The calculation of the moving average begins in the following way:

- The first EXPAVE value (801,123.0) is the same as the first DOLLARS value.
- The second EXPAVE
value (741,731.5) is calculated as follows. Note that because of
rounding and the number of decimal places used, the value derived
in this sample calculation varies slightly from the one displayed
in the report output:
n=3 (number used to calculate weights)

k = 2/(1+n) = 2/4 = 0.5

EXPAVE = (EXPAVE*(1-k))+(new-DOLLARS*k) = (801123*0.5) + (682340*0.50) = 400561.5 + 341170 = 741731.5

- The third EXPAVE
value (753,404.8) is calculated as follows:
EXPAVE = (EXPAVE*(1-k))+(new-DOLLARS*k) = (741731.5*0.5)+(765078*0.50) = 370865.75 + 382539 = 753404.75

For predicted values beyond those supplied, the last EXPAVE value is used as the new data point in the exponential smoothing calculation. The predicted EXPAVE values (starting with 706,741.6) are calculated using the previous average and the new data point. Because the previous average is also used as the new data point, the predicted values are always equal to the last trend value. For example, the previous average for period 13 is 706,741.6, and this is also used as the next data point. Therefore, the average is calculated as follows: (706,741.6 * 0.5) + (706,741.6 * 0.5) = 706,741.6

EXPAVE = (EXPAVE * (1-k)) + (new-DOLLARS * k) = (706741.6*0.5) + (706741.6*0.50) = 353370.8 + 353370.8 = 706741.6

Double exponential smoothing produces an exponential moving average that takes into account the tendency of data to either increase or decrease over time without repeating. This is accomplished by using two equations with two constants.

- The first equation
accounts for the current time period and is a weighted average of
the current data value and the prior average, with an added component
(b) that represents the trend for the previous period. The weight
constant is k:
DOUBLEXP(

`t`) =*k** datavalue(t) + (1-*k*) * ((DOUBLEXP(`t`-1) + b(`t`-1)) - The second equation
is the calculated trend value, and is a weighted average of the
difference between the current and previous average and the trend
for the previous time period. b(
*t*) represents the average trend. The weight constant is g:b(

`t`) = g * (DOUBLEXP(`t`)-DOUBLEXP(`t`-1)) + (1 - g) * (b(`t`-1))

These two equations are solved to derive the smoothed average. The first smoothed average is set to the first data value. The first trend component is set to zero. For choosing the two constants, the best results are usually obtained by minimizing the mean-squared error (MSE) between the data values and the calculated averages. You may need to use nonlinear optimization techniques to find the optimal constants.

The equation used for forecasting beyond the data points with double exponential smoothing is

forecast(t+m) = DOUBLEXP(t) +m* b(t)

where:

`m`- Is the number of time periods ahead for the forecast.

The following defines an integer value named PERIOD to use as the independent variable for the moving average. The double exponential smoothing method estimates the trend in the data points better than the single smoothing method:

SET HISTOGRAM = OFF TABLE FILE CENTSTMT SUM ACTUAL_YTD BY PERIOD ON PERIOD RECAP EXP/D15.1 = FORECAST(ACTUAL_YTD,1,0,'EXPAVE',3); ON PERIOD RECAP DOUBLEXP/D15.1 = FORECAST(ACTUAL_YTD,1,0, 'DOUBLEXP',3,3); WHERE GL_ACCOUNT LIKE '3%%%' END

The output is:

Triple exponential smoothing produces an exponential moving average that takes into account the tendency of data to repeat itself in intervals over time. For example, sales data that is growing and in which 25% of sales always occur during December contains both trend and seasonality. Triple exponential smoothing takes both the trend and seasonality into account by using three equations with three constants.

For triple exponential smoothing you, need to know the number of data points in each time period (designated as L in the following equations). To account for the seasonality, a seasonal index is calculated. The data is divided by the prior season index and then used in calculating the smoothed average.

- The first equation
accounts for the current time period, and is a weighted average
of the current data value divided by the seasonal factor and the
prior average adjusted for the trend for the previous period. The
weight constant is k:
SEASONAL(

`t`) =*k** (datavalue(`t`)/I(`t`-L)) + (1-*k*) * (SEASONAL(`t`-1) + b(`t`-1)) - The second equation
is the calculated trend value, and is a weighted average of the
difference between the current and previous average and the trend
for the previous time period. b(
*t*) represents the average trend. The weight constant is g:b(

`t`) = g * (SEASONAL(`t`)-SEASONAL(`t`-1)) + (1-g) * (b(`t`-1)) - The third equation
is the calculated seasonal index, and is a weighted average of the
current data value divided by the current average and the seasonal
index for the previous season. I(
*t*) represents the average seasonal coefficient. The weight constant is p:I(

`t`) = p * (datavalue(`t`)/SEASONAL(`t`)) + (1 - p) * I(`t`-L)

These equations are solved to derive the triple smoothed average. The first smoothed average is set to the first data value. Initial values for the seasonality factors are calculated based on the maximum number of full periods of data in the data source, while the initial trend is calculated based on two periods of data. These values are calculated with the following steps:

- The initial trend
factor is calculated by the following formula:
b(0) = (1/L) ((y(L+1)-y(1))/L + (y(L+2)-y(2))/L + ... + (y(2L) - y(L))/L )

- The calculation of
the initial seasonality factor is based on the average of the data values
within each period, A(j) (1<=j<=N):
A(j) = ( y((j-1)L+1) + y((j-1)L+2) + ... + y(jL) ) / L

- Then, the initial
periodicity factor is given by the following formula, where N is
the number of full periods available in the data, L is the number
of points per period and n is a point within the period (1<=
n <= L):
I(n) = ( y(n)/A(1) + y(L+n)/A(2) + ... + y((N-1)L+n)/A(N) ) / N

The three constants must be chosen carefully. The best results are usually obtained by choosing the constants to minimize the mean-squared error (MSE) between the data values and the calculated averages. Varying the values of npoint1 and npoint2 affect the results, and some values may produce a better approximation. To search for a better approximation, you may want to find values that minimize the MSE.

The equation used to forecast beyond the last data point with triple exponential smoothing is:

forecast(t+m) = (SEASONAL(t) +m* b(t)) / I(t-L+MOD(m/L))

where:

`m`- Is the number of periods ahead for the forecast.

In the following, the data has seasonality
but no trend. Therefore, *npoint2* is set high (1000) to make
the trend factor negligible in the calculation:

SET HISTOGRAM = OFF TABLE FILE VIDEOTRK SUM TRANSTOT BY TRANSDATE ON TRANSDATE RECAP SEASONAL/D10.1 = FORECAST(TRANSTOT,1,3,'SEASONAL', 3,3,1000,1); WHERE TRANSDATE NE '19910617' END

In the output, *npredict* is
3. Therefore, three periods (nine points, *nperiod * npredict*)
are generated.

The Linear Regression Equation estimates values by assuming that the dependent variable (the new calculated values) and the independent variable (the sort field values) are related by a function that represents a straight line:

y=mx +b

where:

`y`- Is the dependent variable.
`x`- Is the independent variable.
`m`- Is the slope of the line.
`b`- Is the y-intercept.

REGRESS uses a technique called Ordinary Least Squares to calculate
values for *m* and *b* that minimize the sum of the squared
differences between the data and the resulting line.

The following formulas show how *m* and *b* are
calculated.

where:

`n`- Is the number of data points.
`y`- Is the data values (dependent variables).
`x`- Is the sort field values (independent variables).

Trend values, as well as predicted values, are calculated using the regression line equation.

TABLE FILE CAR PRINT MPG BY DEALER_COST WHERE MPG NE 0.0 ON DEALER_COST RECAP FORMPG=FORECAST(MPG,1000,3,'REGRESS'); END

The output is:

DEALER_COSTMPGFORMPG2,886 27 25.51 4,292 25 23.65 4,631 21 23.20 4,915 21 22.82 5,063 23 22.63 5,660 21 21.83 21 21.83 5,800 24 21.65 6,000 24 21.38 7,427 16 19.49 8,300 18 18.33 8,400 18 18.20 10,000 18 16.08 11,000 18 14.75 11,194 9 14.50 14,940 11 9.53 15,940 0 8.21 16,940 0 6.88 17,940 0 5.55

Note:

- Three predicted values of FORMPG are calculated. For values outside the range of the data, new DEALER_COST values are generated by adding the interval value (1,000) to the prior DEALER_COST value.
- There are no MPG values for the generated DEALER_COST values.
- Each FORMPG value
is computed using a regression line, calculated using all of the actual
data values for MPG.
DEALER_COST is the independent variable (x) and MPG is the dependent variable (y). The equation is used to calculate MPGFORECAST trend and predicted values.

In this case, the equation is approximately as follows:

FORMPG = (-0.001323 * DEALER_COST) + 29.32

The predicted values are (the values are not exactly as calculated by FORECAST because of rounding, but they show the calculation process).

DEALER_COST |
Calculation |
FORMPG |
---|---|---|

15,940 |
(-0.001323 * 15,940) + 29.32 |
8.23 |

16,940 |
(-0.001323 * 16,940) + 29.32 |
6.91 |

17,940 |
(-0.001323 * 17,940) + 29.32 |
5.59 |

You can use FORECAST multiple times in one request. However, all FORECAST requests must specify the same sort field, interval, and number of predictions. The only things that can change are the RECAP field, method, field used to calculate the FORECAST values, and number of points to average. If you change any of the other parameters, the new parameters are ignored.

If you want to move a FORECAST column in the report output, use an empty COMPUTE command for the FORECAST field as a placeholder. The data type (I, F, P, D) must be the same in the COMPUTE command and the RECAP command.

To make the report output easier to interpret, you can create a field that indicates whether the FORECAST value in each row is a predicted value. To do this, define a virtual field whose value is a constant other than zero. Rows in the report output that represent actual records in the data source will appear with this constant. Rows that represent predicted values will display zero. You can also propagate this field to a HOLD file.

This example calculates moving averages and exponential averages for both the DOLLARS and BUDDOLLARS fields in the GGSALES data source. The sort field, interval, and number of predictions are the same for all of the calculations.

DEFINE FILE GGSALES SDATE/YYM = DATE; SYEAR/Y = SDATE; SMONTH/M = SDATE; PERIOD/I2 = SMONTH; END TABLE FILE GGSALES SUM DOLLARS AS 'DOLLARS' BUDDOLLARS AS 'BUDGET' BY CATEGORY NOPRINT BY PERIOD AS 'PER' WHERE SYEAR EQ 97 AND CATEGORY EQ 'Coffee' ON PERIOD RECAP DOLMOVAVE/D10.1= FORECAST(DOLLARS,1,0,'MOVAVE',3); ON PERIOD RECAP DOLEXPAVE/D10.1= FORECAST(DOLLARS,1,0,'EXPAVE',4); ON PERIOD RECAP BUDMOVAVE/D10.1 = FORECAST(BUDDOLLARS,1,0,'MOVAVE',3); ON PERIOD RECAP BUDEXPAVE/D10.1 = FORECAST(BUDDOLLARS,1,0,'EXPAVE',4); END

The output is shown in the following image.

The following example places the DOLLARS field after the MOVAVE field by using an empty COMPUTE command as a placeholder for the MOVAVE field. Both the COMPUTE command and the RECAP command specify formats for MOVAVE (of the same data type), but the format of the RECAP command takes precedence.

DEFINE FILE GGSALES SDATE/YYM = DATE; SYEAR/Y = SDATE; SMONTH/M = SDATE; PERIOD/I2 = SMONTH; END TABLE FILE GGSALES SUM UNITS COMPUTE MOVAVE/D10.2 = ; DOLLARS BY CATEGORY BY PERIOD WHERE SYEAR EQ 97 AND CATEGORY EQ 'Coffee' ON PERIOD RECAP MOVAVE/D10.1= FORECAST(DOLLARS,1,3,'MOVAVE',3); END

The output is shown in the following image.

CategoryPERIODUnit SalesMOVAVEDollar SalesCoffee 1 61666 801,123.0 801123 2 54870 741,731.5 682340 3 61608 749,513.7 765078 4 57050 712,897.3 691274 5 59229 725,598.7 720444 6 58466 718,058.3 742457 7 60771 736,718.0 747253 8 54633 715,202.0 655896 9 57829 711,155.3 730317 10 57012 703,541.7 724412 11 51110 691,664.3 620264 12 58981 702,334.7 762328 13 0 694,975.6 0 14 0 719,879.4 0 15 0 705,729.9 0

In the following example, the DATA_ROW virtual field has the value 1 for each row in the data source. It has the value zero for the predicted rows. The PREDICT field is calculated as YES for predicted rows, and NO for rows containing data.

DEFINE FILE CAR DATA_ROW/I1 = 1; END TABLE FILE CAR PRINT DATA_ROW COMPUTE PREDICT/A3 = IF DATA_ROW EQ 1 THEN 'NO' ELSE 'YES' ; MPG BY DEALER_COST WHERE MPG GE 20 ON DEALER_COST RECAP FORMPG/D12.2=FORECAST(MPG,1000,3,'REGRESS'); ON DEALER_COST RECAP MPG =FORECAST(MPG,1000,3,'REGRESS'); END

The output is:

DEALER_COSTDATA_ROWPREDICTMPGFORMPG2,886 1 NO 27.00 25.65 4,292 1 NO 25.00 23.91 4,631 1 NO 21.00 23.49 4,915 1 NO 21.00 23.14 5,063 1 NO 23.00 22.95 5,660 1 NO 21.00 22.21 1 NO 21.00 22.21 5,800 1 NO 24.20 22.04 6,000 1 NO 24.20 21.79 7,000 0 YES 20.56 20.56 8,000 0 YES 19.32 19.32 9,000 0 YES 18.08 18.08

WebFOCUS | |

Feedback |