- author = "Danil Koryakin and Sebastian Otte and Martin V. Butz",
- title = "Inference of time series components by online co-evolution",
- journal = "Genetic Programming and Evolvable Machines",
- year = "2022",
- volume = "23",
- number = "1",
- pages = "7--35",
- month = mar,
- keywords = "genetic algorithms, genetic programming, ANN, Differential evolution, Ensemble, time series identification, Modular recurrent neural networks, Dynamic modules, Non-stationary processes, Online co-evolution, Synchronization",
- ISSN = "1389-2576",
-
URL = "
https://rdcu.be/cpKz0",
-
DOI = "
doi:10.1007/s10710-021-09408-6",
- size = "29 pages",
- abstract = "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 parameterised, 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 synchronises 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.",
-
notes = "Koryakin2021_Article_InferenceOfTimeSeriesComponent
Neuro-Cognitive Modeling, Department of Computer Science, Faculty of Science, University of Tuebingen, Sand 14, 72076 Tuebingen, Germany",
