Function forecasting::ExponentialSmoothingTrendTune(dataValues, noObservations, alpha, beta, alphaLow, alphaUpp, betaLow, betaUpp)


The forecasting::ExponentialSmoothingTrendTune() procedure is a time series forecasting helper procedure of forecasting::ExponentialSmoothingTrend() by computing the \(\alpha\) and \(\beta\) for which the mean squared error is minimized.

Function Prototype

! Provides the alpha for which the mean squared error is minimized.
        dataValues,      ! Input, parameter indexed over time set
        noObservations,  ! Scalar input, length history
        alpha,           ! Scalar output,
        beta,            ! Scalar output,
        alphaLow,        ! Optional input, default 0.01
        alphaUpp,        ! Optional input, default 0.99
        betaLow,         ! Optional input, default 0.01
        betaUpp)         ! Optional input, default 0.99



A one dimensional parameter containing the observations for the first \(T\) elements of the time set.


Specifies the number of elements that belong to the history of the time set. This parameter corresponds to \(T\) in the notation presented in Time Series Forecasting Notation.


beta, i.e. \(\alpha\) and \(\beta\) are scalar output parameters of this procedure. The values for \(\alpha\) and \(\beta\) are such that the mean squared error of the estimates returned by forecasting::ExponentialSmoothingTrend() are minimized.


Lowerbound on \(\alpha\), default 0.01.


Upperbound on \(\alpha\), default 0.99.


Lowerbound on \(\beta\), default 0.01.


Upperbound on \(\beta\), default 0.99.


In order to use this function, the Forecasting system library needs to be added to the application.

Please note that this function performs an optimization step; a nonlinear programming solver should be available and, in an AIMMS PRO environment, it should be run server side.


To further understand about this procedure and library, please use the Demand Forecasting example.