Computer Science – Artificial Intelligence
Scientific paper
2005-07-08
Pattern Recognition Letters 27 (2006), 1353-1360
Computer Science
Artificial Intelligence
Scientific paper
10.1016/j.patrec.2006.01.005
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.
Barbosa Valmir C.
Rigo Luis O. Jr.
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