Function forecasting::ExponentialSmoothingTune(dataValues, noObservations, alpha, alphaLow, alphaUpp)

# forecasting::ExponentialSmoothingTune

The forecasting::ExponentialSmoothingTune() procedure is a time series forecasting helper procedure of forecasting::ExponentialSmoothing() by computing the $$\alpha$$ for which the mean squared error is minimized.

## Function Prototype

forecasting::ExponentialSmoothingTune(
! 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, weight of observation
! that minimizes mean squared error
alphaLow,        ! Optional input, default 0.01
alphaUpp)        ! Optional input, default 0.99


## Arguments

dataValues

A one dimensional parameter containing the observations for the first $$T$$ elements of the time set.

noObservations

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.

alpha

Upon return it provides the weighting factor $$\alpha$$ for which the mean squared error is minimized when using forecasting::ExponentialSmoothing() on the same dataValues.

alphaLow

Lowerbound on $$\alpha$$, default 0.01.

alphaUpp

Upperbound on $$\alpha$$, default 0.99.

Note

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.