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AnaGraph : Adaptive numerical methods for nonstationary time series analysis of time-dependent graphs in context of dynamical systems

Titel Englisch AnaGraph : Adaptive numerical methods for nonstationary time series analysis of time-dependent graphs in context of dynamical systems
Gesuchsteller/in Horenko Illia
Nummer 131845
Förderungsinstrument Projektförderung (Abt. I-III)
Forschungseinrichtung Facoltà di scienze informatiche Università della Svizzera italiana
Hochschule Università della Svizzera italiana – USI
Hauptdisziplin Mathematik
Beginn/Ende 01.10.2010 - 31.10.2013
Bewilligter Betrag 155'744.00
Alle Daten anzeigen

Keywords (8)

time series analysis; discrete stochastic processes; graph inference; adaptive numerical methods; high performance computing; multiscale processes; Markov processes; transfer operator

Lay Summary (Englisch)

Lead
Lay summary
This project AnaGraph is concerned with the development of adaptive numerical strategies for time series analysis and optimization in very large nonstationary graphs, i. e., graphs with the time-varying topology and weights of the edges. The information about the temporal changes of the considered graphs is assumed to be given implicitly through some (possibly incomplete) time series of graph observables. To achieve the main aim of the project, nonstationary persistency-regularized variational formulation of the Markovian inference problem recently developed by the applicant in context of discrete graph inference (for directly observable low-dimensional dynamical processes on small graphs) will be combined with an appropriate dimension reduction strategy and information theory concepts to allow for analysis of large nonstationary graph dynamics under influence of external factors. Adaptive numerical inference methods will be implemented using concepts from PDE numerics and tested on realistic climatological and financial time series.
Direktlink auf Lay Summary Letzte Aktualisierung: 21.02.2013

Verantw. Gesuchsteller/in und weitere Gesuchstellende

Mitarbeitende

Name Institut

Publikationen

Publikation
Low frequency variability in a coupled ocean-sea ice general circulation model of the Southern Ocean.
T.J. O'Kane R. Matear M. Chamberlain J. Risbey I. Horenko and B. Sloyan (2013), Low frequency variability in a coupled ocean-sea ice general circulation model of the Southern Ocean., in ANZIAM J., 54, 200.
An adaptive Markov chain Monte Carlo approach to time series clustering of processes with regime transition behavior.
J. de Wiljes A. Majda and I. Horenko (2013), An adaptive Markov chain Monte Carlo approach to time series clustering of processes with regime transition behavior., in SIAM Journal of Multiscale Modeling and Simulation, 415.
Changes in the meta-stability of the mid-latitude Southern Hemisphere circulation and the utility of non-stationary cluster analysis and split flow blocking indices as diagnostic tools.
T. J. O'Kane J. S. Risbey Ch. Franzke I. Horenko D. P. Monselesan (2013), Changes in the meta-stability of the mid-latitude Southern Hemisphere circulation and the utility of non-stationary cluster analysis and split flow blocking indices as diagnostic tools., in Journal of Atmospherical Science, 824.
Intrinsic and Forced Modes of Low Frequency Variability in Simulated Southern Ocean and Sea Ice Dynamics.
T.J. O'Kane R. Matear M. Chamberlain J. Risbey B. Sloyan and I. Horenko (2013), Intrinsic and Forced Modes of Low Frequency Variability in Simulated Southern Ocean and Sea Ice Dynamics., in Ocean Modelling, 1.
The meta-stability of the mid-latitude Southern Hemisphere circulation.
T.J. O'Kane R. Matear M. Chamberlain J. Risbey I. Horenko and D. Monselesan (2013), The meta-stability of the mid-latitude Southern Hemisphere circulation., in ANZIAM J., 54, 233.
Information Theory, Model Error, and Predictive Skill of Stochastic Models for Complex Nonlinear Systems
Dimitris Giannakis Andrew Majda and Illia Horenko (2012), Information Theory, Model Error, and Predictive Skill of Stochastic Models for Complex Nonlinear Systems, in Physica D, 241(20), 1735-1752.
Supervised Learning Approaches to Classify Stratospheric Warming Events
Christian Blume Katja Matthes and Illia Horenko (2012), Supervised Learning Approaches to Classify Stratospheric Warming Events, in Journal of Atmospherical Sciences, 69, 1824-1840.
Nonstationarity in Multifactor Models of Discrete Jump Processes, Memory, and Application to Cloud Modeling
Horenko I (2011), Nonstationarity in Multifactor Models of Discrete Jump Processes, Memory, and Application to Cloud Modeling, in JOURNAL OF THE ATMOSPHERIC SCIENCES, 68(7), 1493-1506.
Analysis of persistent non-stationary time series and applications
Philipp Metzner Lars Putzig and Illia Horenko, Analysis of persistent non-stationary time series and applications, in CAMCOS, 7(2), 175-229.
Discrete non-homogenous and non-stationary logistic and Markov regression models for spatio-temporal data with unresolved external influences.
J. de Wiljes L. Putzig and I. Horenko, Discrete non-homogenous and non-stationary logistic and Markov regression models for spatio-temporal data with unresolved external influences., in Communications in Applied Mathematics and Computational Science, 1.
On analysis of nonstationary categorical data time series: dynamical dimension reduction, model selection and applications to computational sociology.
I. Horenko, On analysis of nonstationary categorical data time series: dynamical dimension reduction, model selection and applications to computational sociology., in SIAM Mult. Mod. Sim.

Zusammenarbeit

Gruppe / Person Land
Formen der Zusammenarbeit
Australian MetOffice Australien (Ozeanien)
- vertiefter/weiterführender Austausch von Ansätzen, Methoden oder Resultaten
- Publikation
DFG SPP MetStroem Deutschland (Europa)
- vertiefter/weiterführender Austausch von Ansätzen, Methoden oder Resultaten
- Publikation
CSIRO Marine Research Australien (Ozeanien)
- vertiefter/weiterführender Austausch von Ansätzen, Methoden oder Resultaten
- Publikation

Wissenschaftliche Veranstaltungen

Auszeichnungen

Titel Jahr
Offer of a DFG Mercator-Fellowship and a guest-professorship at the Freie Universitaet Berlin 2013
Offer of IPAM/UCLA-fellowship (from the Institute of Pure and Applied Mathematics at UCLA), to become a core participant in the IPAM program on Materials for Sustainable Energy in autumn 2013 (offer declined by the PI, since it would interfere with the teaching obligations of the PI at USI) 2013

Verbundene Projekte

Nummer Titel Start Förderungsinstrument
152979 AnaGraM: Adaptive numerical methods for time series analysis of time-dependent dynamical Graphs in the presence of Missing data 01.06.2014 Projektförderung (Abt. I-III)

Abstract

This project AnaGraph is concerned with the development of adaptive numerical strategies for time series analysis and optimization in very large nonstationary graphs, i. e., graphs with the time-varying topology and weights of the edges. The information about the temporal changes ofthe considered graphs is assumed to be given implicitly through some (possibly incomplete) time series of graph observables. To achieve the main aim of the project, nonstationary persistency-regularized variational formulation of the Markovian inference problem recently developed bythe applicant in context of discrete graph inference (for directly observable low-dimensional dynamical processes on small graphs) will be combined with an appropriate dimension reduction strategy and information theory concepts to allow for analysis of large nonstationary graph dynamics under influence of external factors. Adaptive numerical inference methods will be implemented using concepts from PDE numerics and tested on realistic climatological and financial time series.
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