In this paper a collaborative filter combination for time-series prediction is considered. The basic idea is based on a convex combination of two kernel adaptive filters with different parameters. While the convergence of one filter is fast but not accurate, the convergence of the second one is much more accurate, even if slower. The convex combination of both filters allows to reach good performances in terms of convergence and speed. Some experimental results on the prediction of the Mackey-Glass time-series demonstrate the effectiveness of the proposed approach.
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