Regression for Time Series Settings

Feature Extraction

Observation index

Select this option to include the observation index to capture linear trend.

Observation index squared

Select this option to include the square of the observation index to capture second-order effects.

Observation index cubed

Select this option to include the cube of the observation index to capture third-order effects.

Seasonal dummy variables

Select this setting to generate seasonal dummy variables during feature extraction. The number of seasonal dummy variables corresponds to the Seasonal cycle length specified for the time variable.

This setting is enabled by default.

Time interval for creating seasonal dummy variables

Enter a valid time interval value for creating seasonal dummy variables. If this value is left blank, the time interval specified for the time variable is used. For example, if the time variable for the project uses Week for the time interval, 52 seasonal dummy variables are generated. If you specify Month , then only 12 seasonal dummy variables are generated.

Enter one of the following specification values. Each specification shows the corresponding setting for the Time variable on the Data tab.

Time Interval Specifications and Corresponding Time Variable Intervals

Time interval specification	Time variable setting
year	Year
yearv	ISO 8601
r445yr	Retail 4-4-5 year
r454yr	Retail 4-5-4 year
r544yr	Retail 5-4-4 year
semiyear	Semiyear
r445qtr	Retail 4-4-5 quarter
r454qtr	Retail 4-5-4 quarter
r544qtr	Retail 5-4-4 quarter
quarter	Quarter
month	Month
r445mon	Retail 4-4-5 month
r454mon	Retail 4-5-4 month
r544mon	Retail 5-4-4 month
semimonth	Semimonth
tenday	Ten-day
week	Week
weekv	ISO 8601 week
weekday	Weekday
day	Day
hour	Hour
minute	Minute
second	Second

Model Selection

Number of data points used in the holdout sample

Enter a positive integer to be used as the size of the holdout samplethe number of periods of the most recent data that should be excluded from the parameter estimation. The holdout sample can be used to evaluate the forecasting performance of a candidate model.. The actual holdout sample is the minimum between this value and the Percentage of data points used in the holdout sample . The default value is zero, which means no holdout sample is used.

Percentage of data points used in the holdout sample

Enter a value between 0 and 100 to specify the percentage of the sample that is used for the holdout sample. The actual holdout sample is the minimum between this value and the Number of data points used in the holdout sample . This option is displayed only if Number of data points used in the holdout sample is greater than zero.

Model selection criterion

Choose the statistics of fit to use for selecting the best model in this modeling node. For descriptions for each option, see Descriptions of Model Selection Criteria.

Output Tables

The following tables are automatically generated when running this modeling node. The tables can be saved to another caslib by using the Save Data Node.

• Statistics of Fit (OUTSTAT)
• Descriptive Statistics (OUTSUM)
• User-Defined and Derived Attributes (MERGED_ATTRIBUTES)
• Model Attributes (OUTMODELINFO)
• Model Selection Statistics (OUTSELECT)
• Log Messages (OUTLOG)
• Forecasted Values (OUTFOR)

You can select the following optional tables. Select any tables that you want generated when this node is run. After the node is run, you can use the Save Data Node if you need to save any of these tables.

• Parameter Estimates (OUTEST)
• Event Definitions (OUTEVENT)
• Forecast Model Selection Graph (DIAGNOSEOUTFMSG)
• Model Forecast Components (OUTCOMPONENT)
• Independent Variables (OUTINDEP)
• Event Dummy Variables (OUTEVENTDUMMY)