Abstract:
A time series is generally considered as an ordered sequence of measurements or values
of a variable at equally spaced time intervals. The time series models are useful in many
applications to understand the underlying forces and structure that produced the
observed series of values as well as to forecast future events of the observed
process.Double Exponential Smoothing technique is one of the most important
quantitative techniques in forecasting. The accuracy of forecasting by this technique
depends onparameters associated with the technique.Choosing appropriate values for
^these parameters is very crucial to minimize the error in-forecasting. Normally, trial and
error method is used to determine the optimal values for these parameters even though
non-linear optimization techniques, such as the Levenberg-Marquardt method, are
available, for estimating such parameters but with a considerably high computational
cost. On the other hand, the choice of parameters using trial and error methods requires
calculation of error measures to choose most suitable parameters.In this work a
parameter estimation program known as PEST is used to estimate the optimal values of
parameters for double exponential smoothing technique. PEST is an independent
program that can be used without changing the model in order to estimate the
parameters required for the model. It requires only an executable program that takes the
parameters and produces predictions, based on the given parameters, to match with the
observed values. This study shows that the almost all the error measures in predictions
which were generated by using the parameters estimated by PEST are considerably very
small. It is identified that the estimation of such parameters using PEST is an efficient
and effective technique when compared to trial and error method in estimating
parameters for Double Exponential Smoothing technique.
