Parameter Estimation of Space-Time Model Using Genetic Algorithm

The Space-Time Autoregressive MovingAverage
(STARMA) model family is a statistical inductive
model that can be used to describe stationary (or weak
stationary) space-time processes. However, parameter
estimation of the model often is not easy to obtain
analytically because of the hard computation or the
unknown probability density function underlying the data.
To ease the difficulty, an approach to estimate the parameter
is proposed in this study, i.e. genetic algorithm (GA). GA is
one of the meta-heuristic methods widely used in many
applications including the parameter estimation. The GA is
performed through simulations of various combinations of
selection and crossover parameter chromosomes. The
estimation, then, was carried out by the help of freeware R.
The performance of the GA in estimating parameter is
measured in the sense of the minimum residual sum of
squares and the Akaike Information Criterion (AIC). In
order to have a comparable solution, we employed the
STARMA model of assault arrests in 14 districts of
Northeast Boston (1969-1974) of Pfeifer and Deutsch. The
results show that the performance of the GA is relatively
competitive to the classical method. Since GA is simple to apply, it might be considered as one of the alternative methods for estimating space-time model parameters.