Regression Model Forecasting, Scenario based forecasting, NonLinear Regression Models
Exante / Expost forecasting
 Exante forecasts utilize information that is available in advance such as trend, seasonality, or any calendar variables; however, this also suggests that predictors used for the forecast must also be forecasted beforehand.
 Expost forecasts utilize later information on predictors; they are more useful for studying the behaviors of forecasting models with the future knowledge of predictors.
Scenario based forecasting

It is also possible to make forecasts using a regression model while considering specific scenarios that are bound to happen or that are of interest.

For example, it is possible to make predictions on future changes to the predictors, such as an increasing/decreasing rate of some index.

The example below shows the forecasts for the electricity demand based on two scenarios that are to happen in the future: a temperature of 35 degrees or 15 degrees.

Note that the prediction intervals for the scenariobased forecasting does not include the uncertainty associated with the predictors itself and it would be more certain if the predictors are distributed closer to the sample mean.
NonLinear Regression

Although linear relationships explain many of the real life data, nonlinear models are often more suitable for certain scenarios.

The following plot below represents the annual population of Afganistan throughout the years, where we can see a linear model might not be the most adequate. In fact, there are two knots at this model (1980, 1989), which is when the SovietAfgan war occured.

A nonlinear, in this case, a piecewise model better explains the recent data and is more capable of making accurate forecasts as seen below.