Filling gaps in chaotic time series

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 the degree of compatibility of a filling sequence of data with the reconstructed dynamics. An algorithm for finding highly compatible filling sequences with a reasonable computational effort is then discussed.