Using Forecast.Regression

Dependent Data Column

(Required) Specifies the name of a column returned into the datalayer. It represents data values for the Y-axis.

ID

(Required) A unique element ID.

Independent Data Column

(Required) Specifies the name of a column returned into the datalayer. It represents data values for the X-axis.

Regression Type

(Required) Specifies the type of regression analysis to be applied.

• Select Linear to calculate predictive values based on a trend line.
• Select Autoregressive when attempting to predict an output of a system based on previous outputs. The estimation technique used is based on "Burg's" method. Set the optional Autoregressive Order attribute (below) to define how many previous values to use.
• Select a non-linear regression type (Exponential, Logarithmic, Polynomial, or Power) to display the relationship between dependent and independent variables as a curvilinear function, which may provide more accuracy than a linear regression.

Autoregressive Order

Used only when Regression Type is set to Autoregressive. Specify a value between 1 - 8(default = 3) to define how many previous values have to be observed for analysis.

Forecast Indicator Column ID

Specifies the name of a new column to be added to the datalayer, with value set to True, for each row used in the forecast analysis. If left blank, the column will automatically be named "{ElementId}" + "ForecastIndicator".

Forecast Length

Specifies the actual number of rows to analyze. A value entered that is greater than the total number of rows will be reduced to the total number of rows minus one. If left blank, 20% of the total number of rows will be used.

Forecast Value Column ID

Specifies the name of a new column that will be created in the datalayer to hold each forecast value. If this value is left blank, the forecast value will be added to the value of the Dependent Data Column.