The prediction of visual field deterioration in patients who are suffering from normal tension glaucoma plays an important role in the management of the disease. The Vector Auto-Regressive (VAR) process appears to be an appropriate way of modelling the multivariate time series data from the visual fields. However, standard parameterisation techniques such as the Yule-Walker equations for building a VAR model place a restriction on the minimum length of time series observations. In this paper genetic algorithms are suggested as a way of finding the order and estimating the parameters for the VAR process. To evaluate the effectiveness of this approach, the VAR process in S-Plus, the Holt-Winters forecasting method, and a pure noise model are applied to the same set of visual field data.
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