  Correlated time series are time series that, by virtue of the underlying
process to which they refer, are expected to influence each other strongly. We
introduce a novel approach to handle such time series, one that models their
interaction as a two-dimensional cellular automaton and therefore allows them
to be treated as a single entity. We apply our approach to the problems of
filling gaps and predicting values in rainfall time series. Computational
results show that the new approach compares favorably to Kalman smoothing and
filtering.
