MODEL Subcommand (TSMODEL command)

The MODEL subcommand is required and is used to signal the start of a model specification, as well as to specify model variable(s).

  • All variables must be numeric. Any string variables are filtered out, with a warning.
  • Variables that define split-file groups are filtered out of all variable lists, with a warning.
  • ALL and TO can be used in dependent and independent variable lists. ALL represents all variables in the active dataset. TO refers to a range of variables in the active dataset.

Note: Specification of the model type is optional and is accomplished by including an EXPERTMODELER, EXSMOOTH, or ARIMA subcommand following the MODEL subcommand. If the model type is not specified, the best-fitting ARIMA or exponential smoothing model will automatically be selected by the Expert Modeler, which is equivalent to specifying /EXPERTMODELER TYPE=[ARIMA EXSMOOTH]. If you are unsure of which type of model to choose, or you want Expert Modeler to choose for you, use the MODEL subcommand without a model type subcommand.

Example

TSMODEL
  /MODEL DEPENDENT=store1 TO store100
         INDEPENDENT=adspending
         OUTFILE='/models/stores.xml'
         PREFIX='Expert'.
  • DEPENDENT specifies that variables store1 thru store100 are to be modeled. The keyword TO refers to the order of the variables in the active dataset.
  • The INDEPENDENT keyword specifies that the variable adspending is to be considered as a predictor variable.
  • OUTFILE specifies that the resulting models are to be stored in the file /models/stores.xml.
  • PREFIX specifies that the models will be named Expert_1 (the model for store1), Expert_2 (the model for store2), etc.
  • The absence of a model type subcommand (EXPERTMODELER, ARIMA, or EXSMOOTH) means that the Expert Modeler will be used to find the best-fitting exponential smoothing or ARIMA model.

DEPENDENT Keyword

The DEPENDENT keyword is used to specify one or more dependent variables and is required.

  • At least one dependent variable must be specified.
  • Repeated instances of the same variable are filtered out of the list.
  • A separate model is generated for each dependent variable in each MODEL block. Identical dependent variables in separate MODEL blocks generate separate models.

INDEPENDENT Keyword

The INDEPENDENT keyword is used to specify one or more optional independent variables.

  • Order of variables within the independent variable list matters when the Expert Modeler is used. The Expert Modeler drops nonsignificant independent variables one at a time, starting with the last variable in the list.
  • In the case of custom ARIMA models, all independent variables are included in the model.
  • Repeated instances of an independent variable are filtered out of the list. For example, a b a c a is equivalent to a b c. Assuming that the active dataset contains variables a, b, and c, ALL is also equivalent to a b c.
  • An independent variable that also appears in the dependent variable list is treated solely as a dependent variable and is excluded from consideration as an independent variable for the model involving that particular dependent variable. For example, if you specify DEPENDENT=a b and INDEPENDENT=a c, then c is the only independent variable when modeling a, but a and c are both used as independent variables when modeling b.

Events

  • Event variables are special independent variables that are used to model effects of occurrences such as a flood, strike, or introduction of a new product line. Any abrupt shift in the level of the dependent series can be modeled by using event variables.
  • To designate an independent variable as an event variable, specify [E] following the name of the variable. For example, INDEPENDENT=strike [E] indicates that the variable strike is an event variable.
  • When the Expert Modeler is used, event variables enter the model with linear terms only (as opposed to transfer functions). Thus, event designation can save processing time and guard against overfitting when using the Expert Modeler.
  • Custom ARIMA models ignore event designation.
  • For event variables, the presence of an effect is indicated by cases with a value of 1. Cases with values other than 1 are treated as indicating no effect.
  • The effect of an event can last a single time period or several periods. If the effect lasts several periods, the corresponding event variable should contain a series of consecutive 1s.
  • Event designation applies only to the variable that immediately precedes the designation in the independent variable list. For example, x1 x2 x3 [E] and x1 TO x3 [E] designate x3 (only) as an event variable. If event designation follows ALL, it applies to all independent variables.
  • If a variable appears more than once in the independent variable list, the event specification for the last instance of the variable is honored. For example, if ALL x1 [E] is specified, x1 is treated as an event variable; if ALL [E] X1 is specified, x1 is treated as an ordinary predictor.

OUTFILE and OUTPMML Keywords

The OUTFILE and OUTPMML keywords are used to save models to an external file. Saved models can be used to obtain updated forecasts, based on more current data, using the TSAPPLY command. See the topic TSAPPLY for more information.

  • OUTFILE saves an XML file that can be used with other IBM® SPSS® applications.
  • OUTPMML saves a PMML-compliant XML file that can be used with PMML-compliant applications, including IBM SPSS applications.
  • Models are written to an XML file. Each model is assigned a unique name and a description that includes the name of the dependent variable and the model type.
  • The filename must be specified in full. No extension is supplied.
  • If two or more MODEL subcommands (within a single invocation of TSMODEL) specify the same external file, all models that are created by those MODEL subcommands are saved to that file.
  • OUTFILE and OUTPMML are ignored with a warning if SPLIT FILE is in effect.

PREFIX Keyword

Models are assigned unique names consisting of a customizable prefix, along with an integer suffix. The PREFIX keyword is used to specify a custom prefix.

  • Custom prefixes must be specified in quotes—for example, PREFIX='ExpertArima'. The default prefix is ‘Model’.
  • Integer suffixes are unique across the set of models that have the same prefix; for example, Model_1, Model_2. Models with different prefixes, however, can have the same integer suffix—for example, CustomArima_1, ExpertArima_1.