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Inference of time series components by online co-evolution

机译：Inference of time series components by online co-evolution

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摘要

Time series data is often composed of a multitude of individual, superimposed dynamics. We propose a novel algorithm for inferring time series compositions through evolutionary synchronization of modular networks (ESMoN). ESMoN orchestrates a set of trained dynamic modules, assuming that some of those modules' dynamics, suitably parameterized, will be present in the targeted time series. With the help of iterative co-evolution techniques, ESMoN optimizes the activities of its modules dynamically, which effectively synchronizes the system with the unfolding time series signal and distributes the dynamic subcomponents present in the time series over the respective modules. We show that ESMoN can adapt modules of different types. Moreover, it is able to precisely identify the signal components of various time series dynamics. We thus expect that ESMoN will be useful also in other domains-including, for example, medical, physical, and behavioral data domains-where the data is composed of known signal sources.

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