g_forecast(G;S;O;X;VV;M;OPTS;R)

Shows fitted values and forecasts future values of a time series X using the model object in the M argument. (Available as of version 19.14)

Function type

Vector only

Syntax

g_forecast(G;S;O;X;VV;M;OPTS;R)

Input

Argument Type Description
G any A space- or comma-separated list of column names

Rows are in the same group if their values for all of the columns listed in G are the same.

If G is omitted, all rows are considered to be in the same group.

If any of the columns listed in G contain N/A, the N/A value is considered a valid grouping value.
S integer The name of a column in which every row evaluates to a 1 or 0, which determines whether or not that row is selected to be included in the calculation

If S is omitted, all rows will be considered by the function (subject to any prior row selections).

If any of the values in S are neither 1 nor 0, an error is returned.
O integer A space- or comma-separated list of column names that determine the row order within a particular group

If O is omitted, the order is the current display order of the table.

If any of the values in O are N/A, an error is returned.
X any A column name

Represents a time series.
VV list-value An optional list of independent variables in the case of a model M (such as ARIMAX) that incorporates such variables. For models with exogenous variables, the same VV argument, in the same order, must be specified in g_forecast(). If there are no independent variables (such as for ARIMA models returned by g_arima()), leave this argument empty.
M list-value The type of model. g_forecast() supports all sarimax and autoarima models returned from g_sarimax().( Available as of version 19.35)
OPTS list-value Left empty for ARIMA models. There will be options for future models.
R text Optional for ARIMA values.
  • 'value' – the forecast value (the default value)
  • 'se' – standard error of the forecast value

Return Value

For every row in each group defined by G, g_forecast() returns fitted values and future values of the time series X using the model object in M.

Example

The following example shows how to use g_forecast() with an ARIMA model. In order to forecast future values, it is necessary to expand the last row of each group G by the number of rows to be forecast, after the model is computed on the unexpanded data. This can be done with <sel value="1+N*g_last1(B;S;O)" expand="1"/>

<defblock name="arima_forecast" breaks="" var=""  p="0" d="0" q="0" steps="0">
  <if test="{@var=''}">
    <signal msg="arima_forecast: var= must be specified"/>
  </if>
  <let modelname="{@var}_arima_{@p}_{@d}_{@q}"
       fcname="{@var}_forecast" 
       fcename="{@var}_forecast_se">
    <willbe name="{@modelname}" value="g_arima({@breaks};;;{@var};{@p};{@d};{@q};)"/>
    <sel value="1+{@steps}*g_last1({@breaks};;)" expand="1"/>
    <willbe name="{@fcname}" value="g_forecast({@breaks};;;{@var};;{@modelname};;)"/>
    <willbe name="{@fcename}" value="g_forecast({@breaks};;;{@var};;{@modelname};;'se')"/>
  </let>
</defblock>
<table>
16.9969;16.5969;16.3012;16.1018;17.0998;16.8997;16.8002;17.4001;
17.0998;16.9996;16.6991;17.3993;17.2002;17.4013;17.4029;17.0015;
17.2965;17.1966;17.4013;16.802;17.1;17.3995;17.3995;17.5008;17.3998;
17.6;17.4;17.3;17;17.8;17.5;18.1;17.5;17.4;17.4;17.1;17.6;17.7;17.4;
17.8;17.6;17.5;16.5;17.8;17.3;17.3;17.1;17.4;16.9;17.3;17.6;16.9;
16.7;16.8;16.8;17.2;16.8;17.6;17.2;16.6;17.1;16.9;16.6;18;17.2;17.3;
17;16.9;17.3;16.8;17.3;17.4;17.7;16.8;16.9;17;16.9;17;16.6;16.7;16.8;
16.7;16.4;16.5;16.4;16.6;16.5;16.7;16.4;16.4;16.2;16.4;16.3;16.4;17;
16.9;17.1;17.1;16.7;16.9;16.5;17.2;16.4;17;17;16.7;16.2;16.6;16.9;
16.5;16.6;16.6;17;17.1;17.1;16.7;16.8;16.3;16.6;16.8;16.9;17.1;16.8;
17;17.2;17.3;17.2;17.3;17.2;17.2;17.5;16.9;16.9;16.9;17.0001;16.4999;
16.6996;16.8001;16.7013;16.6985;16.6005;16.5;17;16.7;16.7;16.9;17.4;
17.1;17;16.8;17.2;17.2;17.4;17.2;16.9;16.8;17;17.4;17.2;17.2;17.1;
17.1;17.1;17.4;17.2;16.9;16.9;17;16.7;16.9;17.3;17.8;17.8;17.6;17.5;
17;16.8999;17.1;17.2002;17.3998;17.5001;17.9;17;17;17;17.2;17.3;17.4;
17.4;17.0001;18.0001;18.2001;17.5999;17.7996;17.6992;17.2002;17.4027
</table>
<call block="arima_forecast" var="c1" p="3" d="1" q="3" steps="10"/>