- Function forecasting::ExponentialSmoothingTrendSeasonalityTune(dataValues, noObservations, alpha, beta, gamma, periodLength, alphaLow, alphaUpp, betaLow, betaUpp, gammaLow, gammaUpp)
forecasting::ExponentialSmoothingTrendSeasonalityTune
The forecasting::ExponentialSmoothingTrendSeasonalityTune()
procedure
is a time series forecasting helper procedure of forecasting::ExponentialSmoothingTrendSeasonality()
by computing
the \(\alpha\), \(\beta\), and \(\gamma\) for which the mean
squared error is minimized.
Function Prototype
forecasting::ExponentialSmoothingTrendSeasonalityTune(
! 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,
gamma, ! Scalar output,
periodLength, ! Scalar input, length of season
alphaLow, ! Optional input, default 0.01
alphaUpp, ! Optional input, default 0.99
betaLow, ! Optional input, default 0.01
betaUpp, ! Optional input, default 0.99
gammaLow, ! Optional input, default 0.01
gammaUpp) ! 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,
beta, gamma, i.e. \(\alpha\), \(\beta\), and \(\gamma\) are scalar output parameters of this procedure. The values for \(\alpha\), \(\beta\), and \(\gamma\) are such that the mean squared error of the estimates returned by
forecasting::ExponentialSmoothingTrendSeasonality()
are minimized.- periodLength
Specifies the period length.
- alphaLow
Lowerbound on \(\alpha\), default 0.01.
- alphaUpp
Upperbound on \(\alpha\), default 0.99.
- betaLow
Lowerbound on \(\beta\), default 0.01.
- betaUpp
Upperbound on \(\beta\), default 0.99.
- gammaLow
Lowerbound on \(\gamma\), default 0.01.
- gammaUpp
Upperbound on \(\gamma\), 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.
Example
To further understand about this procedure and library, please use the Demand Forecasting example.