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.

时间序列数据通常由大量单独的、叠加的动态组成。我们提出了一种通过模块化网络（ESMoN）的进化同步来推断时间序列组成的新算法。ESMoN 编排了一组经过训练的动态模块，假设这些模块的一些动态，适当地参数化，将出现在目标时间序列中。在迭代协同进化技术的帮助下，ESMoN 动态优化其模块的活动，有效地将系统与展开的时间序列信号同步，并将时间序列中存在的动态子组件分布在各个模块上。我们表明 ESMoN 可以适应不同类型的模块。此外，它能够精确识别各种时间序列动态的信号分量。