9,762 research outputs found
Statistical Mechanics and Information-Theoretic Perspectives on Complexity in the Earth System
Peer reviewedPublisher PD
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Identifying causal gateways and mediators in complex spatio-temporal systems
Identifying causal gateways and mediators in complex spatio-temporal systems
J.R. received support by the German National Academic Foundation (Studienstiftung), a Humboldt University Postdoctoral Fellowship, and the German Federal Ministry of Science and Education (Young Investigators Group CoSy-CC2, grant no. 01LN1306A). J.F.D. thanks the Stordalen Foundation and BMBF (project GLUES) for financial support. D.H. has been funded by grant ERC-CZ CORES LL-1201 of the Czech Ministry of Education. M.P. and N.J. received funding from the Czech Science Foundation project No. P303-14-02634S and from the Czech Ministry of Education, Youth and Sports, project No. DAAD-15-30. J.H. was supported by the Czech Science Foundation project GA13-23940S and Czech Health Research Council project NV15-29835A. We thank Mary Lindsey from the National Oceanic and Atmospheric Administration for her kind help with Fig. 4e. NCEP Reanalysis data provided by NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, from their web site at http://www.esrl.noaa.gov/psd/.Peer reviewedPublisher PD
Vulnerability of hydropower installations to climate change : preliminary study
The climate trends observed worldwide over the past few decades appear to corroborate the concerns of the scientific community about the many threats posed by global warming. Future changes of the current climate are expected to occur on different scales all around the globe, hence modifying the environmental background on the basis of which technological installations have been designed and operated. This can potentially threat the safety of the installations as well as their. The development of suitable tools aiming to predict the impact of climate change on technological installations is then essential in the wider context of climate change mitigation. Hydropower installations play often a crucial role not only as a long-term renewable resource of energy but also for flood control and water supply in the case of droughts. All these aspects highlight the increasing importance of such installations as well as their growing vulnerability to natural hazards. It is hence essential to enlarge the current understanding of the interaction mechanisms between such installations and the changing surrounding environment in order to take adequate measures for climate change adaptation and ensure the future safety and productivity of hydropower production. The current study aims to provide a novel model for the evaluation of the impact of climate change on the safety of hydropower stations. The approach adopted allows to include in the model the uncertainty inevitably associated with the input variables and to propagate such uncertainty within the analysis. The model proposed is finally applied to a realistic case-study in order to highlight its potential and limitations
Multiscale Information Decomposition: Exact Computation for Multivariate Gaussian Processes
Exploiting the theory of state space models, we derive the exact expressions
of the information transfer, as well as redundant and synergistic transfer, for
coupled Gaussian processes observed at multiple temporal scales. All of the
terms, constituting the frameworks known as interaction information
decomposition and partial information decomposition, can thus be analytically
obtained for different time scales from the parameters of the VAR model that
fits the processes. We report the application of the proposed methodology
firstly to benchmark Gaussian systems, showing that this class of systems may
generate patterns of information decomposition characterized by mainly
redundant or synergistic information transfer persisting across multiple time
scales or even by the alternating prevalence of redundant and synergistic
source interaction depending on the time scale. Then, we apply our method to an
important topic in neuroscience, i.e., the detection of causal interactions in
human epilepsy networks, for which we show the relevance of partial information
decomposition to the detection of multiscale information transfer spreading
from the seizure onset zone
Unified functional network and nonlinear time series analysis for complex systems science: The pyunicorn package
We introduce the \texttt{pyunicorn} (Pythonic unified complex network and
recurrence analysis toolbox) open source software package for applying and
combining modern methods of data analysis and modeling from complex network
theory and nonlinear time series analysis. \texttt{pyunicorn} is a fully
object-oriented and easily parallelizable package written in the language
Python. It allows for the construction of functional networks such as climate
networks in climatology or functional brain networks in neuroscience
representing the structure of statistical interrelationships in large data sets
of time series and, subsequently, investigating this structure using advanced
methods of complex network theory such as measures and models for spatial
networks, networks of interacting networks, node-weighted statistics or network
surrogates. Additionally, \texttt{pyunicorn} provides insights into the
nonlinear dynamics of complex systems as recorded in uni- and multivariate time
series from a non-traditional perspective by means of recurrence quantification
analysis (RQA), recurrence networks, visibility graphs and construction of
surrogate time series. The range of possible applications of the library is
outlined, drawing on several examples mainly from the field of climatology.Comment: 28 pages, 17 figure
Optimal model-free prediction from multivariate time series
Forecasting a time series from multivariate predictors constitutes a
challenging problem, especially using model-free approaches. Most techniques,
such as nearest-neighbor prediction, quickly suffer from the curse of
dimensionality and overfitting for more than a few predictors which has limited
their application mostly to the univariate case. Therefore, selection
strategies are needed that harness the available information as efficiently as
possible. Since often the right combination of predictors matters, ideally all
subsets of possible predictors should be tested for their predictive power, but
the exponentially growing number of combinations makes such an approach
computationally prohibitive. Here a prediction scheme that overcomes this
strong limitation is introduced utilizing a causal pre-selection step which
drastically reduces the number of possible predictors to the most predictive
set of causal drivers making a globally optimal search scheme tractable. The
information-theoretic optimality is derived and practical selection criteria
are discussed. As demonstrated for multivariate nonlinear stochastic delay
processes, the optimal scheme can even be less computationally expensive than
commonly used sub-optimal schemes like forward selection. The method suggests a
general framework to apply the optimal model-free approach to select variables
and subsequently fit a model to further improve a prediction or learn
statistical dependencies. The performance of this framework is illustrated on a
climatological index of El Ni\~no Southern Oscillation.Comment: 14 pages, 9 figure
A Survey on Causal Discovery Methods for Temporal and Non-Temporal Data
Causal Discovery (CD) is the process of identifying the cause-effect
relationships among the variables from data. Over the years, several methods
have been developed primarily based on the statistical properties of data to
uncover the underlying causal mechanism. In this study we introduce the common
terminologies in causal discovery, and provide a comprehensive discussion of
the approaches designed to identify the causal edges in different settings. We
further discuss some of the benchmark datasets available for evaluating the
performance of the causal discovery algorithms, available tools to perform
causal discovery readily, and the common metrics used to evaluate these
methods. Finally, we conclude by presenting the common challenges involved in
CD and also, discuss the applications of CD in multiple areas of interest
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