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.
the required datasets have been imported into the process
the data used for the model has been well prepared.
the time series and exogenous inputs are non-categorical.
Along with the Options tab, where the task can be configured, the following additional tabs are provided:
Documentation tab where you can document your task,
Parametric options tab where you can configure process variables instead of fixed values. Parametric equivalents are expressed in italics in this page (PO).
Models & Results tabs, where you can see the output of the task computation. See the Results table below.
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 left-hand 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 options
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.
Time series attributes (ORDERED)
The ordered attribute which will be used to perform auto regression.
The number of previous values to be used in the calculation for the time series attribute.
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.
The number of previous values to be used in the calculation for each exogenous attribute.
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.
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.
Select a Time attribute from the drop-down list to specify the temporal variable to be used. If not specified, row numbers will be used to establish the time relationship.
The length of each time interval.
Select the unit of time required. Possible values are:
The results of the Auto Regressive task can be viewed in two separate 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.