Publication type: Conference paper
Type of review: Peer review (abstract)
Title: Forecasting correlation structures
Authors: Schüle, Martin
Ott, Thomas
Schwendner, Peter
Proceedings: Proceedings of the 2016 international symposium on nonlinear theory and its applications
Conference details: 2016 International Symposium on Nonlinear Theory and Its Applications (NOLTA2016), Yugawara, Japan, 27-30 November 2016
Issue Date: 2016
Publisher / Ed. Institution: IEICE
Language: English
Subjects: Complex systems; Forecasting; Econometrics
Subject (DDC): 510: Mathematics
Abstract: Often the signature of a complex system is a couple of empirically found time series. As the exact processes generating these series are often unknown, one contents oneself with mere data analysis, i.e., an analysis of the statistical features of the time series. Beyond the individual statistical characteristics of the time series, a key tool to investigate the structural behaviour of the complex system is considering the correlation structure, i.e. the system of pairwise correlations between the time series.
Fulltext version: Published version
License (according to publishing contract): Licence according to publishing contract
Departement: School of Management and Law
Organisational Unit: Institute of Computational Life Sciences (ICLS)
Appears in collections:Publikationen School of Management and Law

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