Complexity

Complexity in Neural and Financial Systems: From Time-Series to Networks


Status
Published

1IMT Institute for Advanced Studies Lucca, Lucca, Italy

2University of Rome "Sapienza", Rome, Italy

3Lorentz Institute for Theoretical Physics, Leiden, Netherlands

4Santa Lucia Foundation, Rome, Italy

5Italian Institute of Technology, Genoa, Italy

6University College London, London, UK


Complexity in Neural and Financial Systems: From Time-Series to Networks

Description

In real world, the functioning of most systems can be described in terms of temporal fluctuations of a collection of unitary constituents. This allows their “activity” to be represented in terms of time-series, thus leading to the definition of characteristic patterns of self-organization to be revealed only by employing multiple time-series analysis. Prominent examples are offered by neural and financial systems as brain neurovascular and neurophysiological signals as well as stock prices fluctuations witness.

The usual method of analysis prescribes to, first, cross-correlating the series and, then, applying a threshold to associate a network to the system of interest. Unfortunately, this kind of approach has been proven to be unsatisfactory under several respects, thus leaving an open question: what is the best strategy to unearth fundamental information of such systems?

Answering this urgent question would represent a first step towards the resolution of a more challenging issue: the identification of clusters whose units are characterized by significantly correlated activities. This, in turn, would shed light on the mechanisms driving the evolution of systems characterized by highly nontrivial patterns of dynamic self-organization, as the neural and financial ones.

The choice of focusing on these two kinds of systems is dictated by the importance that topics like portfolios risk-minimization, brain-modules detection, and the like have gained in recent years, in turn allowing researchers to access unprecedented amounts of data.

This special issue is intended to collect contributions proposing novel techniques for the analysis of systems described by multiple time-series as functional and structural brain data, stock prices and stock market indices, and interbank and trade networks.

Potential topics include but are not limited to the following:

  • Null models for the analysis of time-series and correlation matrices
  • Noise reduction and filtering techniques for correlation matrices
  • Correlation-based community detection algorithms
  • Mapping techniques between correlation matrices and networks
  • Mapping techniques between time-series and networks
  • Filtering techniques for networks
  • Identification of precursors of stock market movements
  • Detection of early warning signals of financial and economic critical events
  • Testing-causality techniques in functional brain data
  • Statistical methods for inverse problems in functional and structural brain data

Articles

  • Special Issue
  • - Volume 2018
  • - Article ID 3132940
  • - Editorial

Complexity in Neural and Financial Systems: From Time-Series to Networks

Tiziano Squartini | Andrea Gabrielli | ... | Fabio Caccioli
  • Special Issue
  • - Volume 2018
  • - Article ID 2825948
  • - Research Article

A Comparative Analysis of the Predictive Abilities of Economic Complexity Metrics Using International Trade Network

Hao Liao | Alexandre Vidmer
  • Special Issue
  • - Volume 2017
  • - Article ID 8581365
  • - Research Article

Connecting Patterns Inspire Link Prediction in Complex Networks

Ming-Yang Zhou | Hao Liao | ... | Zong-Wen Wei
  • Special Issue
  • - Volume 2017
  • - Article ID 4518429
  • - Research Article

Sparse Causality Network Retrieval from Short Time Series

Tomaso Aste | T. Di Matteo
  • Special Issue
  • - Volume 2017
  • - Article ID 7190758
  • - Research Article

A Novel Synchronization-Based Approach for Functional Connectivity Analysis

Angela Lombardi | Sabina Tangaro | ... | Cataldo Guaragnella
  • Special Issue
  • - Volume 2017
  • - Article ID 9586064
  • - Research Article

The Multiplex Dependency Structure of Financial Markets

Nicolò Musmeci | Vincenzo Nicosia | ... | Vito Latora
  • Special Issue
  • - Volume 2017
  • - Article ID 7259032
  • - Research Article

Evolutionary Network Games: Equilibria from Imitation and Best Response Dynamics

Giulio Cimini
  • Special Issue
  • - Volume 2017
  • - Article ID 1580526
  • - Research Article

Predicting the Rise of EU Right-Wing Populism in Response to Unbalanced Immigration

Boris Podobnik | Marko Jusup | ... | H. E. Stanley
Complexity
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 Journal metrics
Acceptance rate43%
Submission to final decision64 days
Acceptance to publication35 days
CiteScore3.200
Impact Factor2.462
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