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| AutoregressiveOrder | | Used when Autoregressive regression type is selected and specifies the parameter that helps to define how many previous values have to be observed for prediction. Values can range from one to total number of rows minus one, with a default value of 3. |
| DependentDataColumn | | (Required) Set this to the name of a column returned into the datalayer. It represents data values for the Y-axis. |
| ForecastIndicatorColumnID | | 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. |
| ForecastLength | | The actual number of rows to predict. A value entered that is greater than the total number of rows will be reduced to total number of rows minus one. If left blank, 20% of the total number of rows will be used. |
| ForecastValueColumnID | | The name of a new column that will be created in the datalayer to hold each forecast value. When working with Forecast.Regression and Forecast.Time Series Decomposition, if this value is left blank, the forecast values will be added to the value of the Dependent Data Column. |
| ID | | (Required) The ID attribute is a pervasive attribute that uniquely identifies an element within a definition file. The ID needs to be a unique value within the definition. |
| IndependentDataColumn | | (Required) Set this to the name of a column returned into the datalayer. It represents data values for the X-axis. |
| RegressionType | | Power |
| Polynomial5 |
| Polynomial4 |
| Polynomial3 |
| Polynomial2 |
| Logarithmic |
| Linear |
| Exponential |
| Autoregressive |
| (Required) Specifies the type of regression analysis to be applied:
Select Linear Regression 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 Autoregressive type uses the "Burg" method.
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. |