---
title: "Using Forecast.Regression"
slug: "using-forecastregression"
updated: 2023-04-19T22:58:38Z
published: 2023-04-19T22:58:38Z
canonical: "docs.interprose.com/using-forecastregression"
stale: true
---

> ## Documentation Index
> Fetch the complete documentation index at: https://docs.interprose.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Using Forecast.Regression

The Forecast.Regression element uses one of several regression analysis functions. Regression analysis is recommended when the focus is on a relationship between a dependent value and one or more independent values. It's useful, for example, when values in the X-axis are "uneven" (are separated by varying differences).

![](https://cdn.document360.io/6c7303ca-5ffc-4149-8868-8ea498eee635/Images/Documentation/RB_57.png?sv=2026-02-06&spr=https&st=2026-10-31T21%3A36%3A42Z&se=2026-10-31T22%3A36%3A42Z&sr=c&sp=r&sig=IV6n0pUX1Hz21q0coqJtgfXlOsrN7h5fe28Xg3qZOg0%3D)

The Forecast.Regression element is a child of any datalayer, as shown above, and its attributes include:

| **Attribute** | **Description** |
| --- | --- |
| 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. |