Filling Gaps in Chaotic Time Series
Francesco Paparella
Abstract
We propose a method for filling arbitrarily wide gaps in deterministic time series. Crucial to the method is the ability to apply Takens' theorem in order to reconstruct the dynamics underlying the time series. We introduce a functional to evaluate how compatible is a filling sequence of data with the reconstructed dynamics. An algorithm for minimizing the functional with a reasonable computational effort is then discussed.
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