Using Auto Regressive to solve Regression Problems
The Auto Regressive task predicts the future values assumed by a signal over time, principally based on its past behavior.
Prerequisites
you must have created a flow;
the required datasets must have been imported into the flow;
the data used for the analysis must have been well prepared;
the time series and exogenous inputs are noncategorical.
Additional tabs
The Models tab, where the coefficient values are displayed, along with any periods that have been retrieved, and other statistical parameters, such as minimum and maximum.
The Results tab, where statistics on the AR computation are displayed, such as the execution time.
Procedure
Drag and drop the Auto Regressive task onto the stage.
Connect a task, which contains the attributes from which you want to create the model, to the new task.
Double click the Auto Regressive task. The lefthand pane displays a list of all the available attributes in the dataset, which can be ordered and searched as required.
Configure the options described in the table below.
Save and compute the task.
Auto Regressive basic options  
Parameter Name  Description 

Time series attributes (ORDERED)  The ordered attribute which will be used to perform auto regression. 
Exogenous attributes (ORDERED)  Additional attributes that need to be considered when performing auto regression predictions. For example, the external temperature could be considered an exogenous attribute when calculating the consumption of energy in heating a house. 
ID Attributes (NOMINAL)  The nominal key attributes that define the rows that will contain the values to be used. Several key attributes can be used: each different tuple of values assumed by the key variables identifies a set of rows concerning a separate model. Consequently the task builds as many models as the different tuples of the key variables. Drag and drop here the key attributes. Rulex will create a different sequence for each key attribute value. Instead of manually dragging and dropping attributes, they can be defined via a filtered list. 
Interval dimension for time series attributes  The number of previous values to be used in the calculation for the time series attribute. 
Interval dimension for exogenous attributes  The number of previous values to be used in the calculation for each exogenous attribute. 
Time attribute  Select a Time attribute from the dropdown list to specify the temporal variable to be used. If not specified, row numbers will be used to establish the time relationship. 
Time lag  The length of each time interval. 
Time unit  Select the unit of time required. Possible values are:

Auto Regressive advanced options  
Parameter Name  Description 

Percentage of constant values for switching to Croston model  When the percentage specified is exceeded the Croston model is used instead of the auto regressive model in the calculation. 
Percentage tolerance for switching to Croston model  The tolerance percentage in the range of variation for constant values. When this percentage is not exceeded, the values are considered constant when deciding if the Croston model is to be used or not. 
Obsolescence coefficients  The column that contains the obsolescence coefficient to be applied to the time series. 
Smoothing function on time series  The function to be used to smooth the values in the time series. Possible values are:

Interval dimension on time series for rolling mean  If you want to use the rolling mean of past values instead of single values, specify here how many values will be contained in each rolling value group. The number of groups used is specified in the Interval dimension option. 