Publication type: Conference paper
Type of review: Peer review (abstract)
Title: Self-organized division of labor in networks of forecasting models for time series with regime switches
Authors: Gygax, Gregory
Füchslin, Rudolf Marcel
Ott, Thomas
et. al: No
Proceedings: Proceedings of the NOLTA 2020 Conference
Pages: 278
Pages to: 281
Conference details: 2020 International Symposium on Nonlinear Theory and Its Applications (NOLTA2020), Online Conference, 16-19 November 2020
Issue Date: Nov-2020
Language: English
Subjects: Self-organization; Resilient machine learning
Subject (DDC): 006: Special computer methods
Abstract: We present the idea of a self-organized division of labor in networks of forecasting models. We find that the principles of self-organizing maps provide a good starting point for building resilient machine learning systems based on our idea. The potential of the idea, benefits and challenges are discussed by means of two toy-like problems.
Fulltext version: Published version
License (according to publishing contract): Licence according to publishing contract
Departement: Life Sciences and Facility Management
Organisational Unit: Institute of Applied Simulation (IAS)
Appears in collections:Publikationen Life Sciences und Facility Management

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