相关参考文献
注意:仅列出部分参考文献,下载原文获取全部文献信息。Extreme tail network analysis of cryptocurrencies and trading strategies
Syed Jawad Hussain Shahzad et al.
FINANCE RESEARCH LETTERS (2022)
Where to go? Predicting next location in IoT environment
Hao Lin et al.
FRONTIERS OF COMPUTER SCIENCE (2021)
An interpretable Neural Fuzzy Hammerstein-Wiener network for stock price prediction
Chen Xie et al.
INFORMATION SCIENCES (2021)
Attention based simplified deep residual network for citywide crowd flows prediction
Genan Dai et al.
FRONTIERS OF COMPUTER SCIENCE (2021)
Advantages of direct input-to-output connections in neural networks: The Elman network for stock index forecasting
Yaoli Wang et al.
INFORMATION SCIENCES (2021)
A novel graph convolutional feature based convolutional neural network for stock trend prediction
Wei Chen et al.
INFORMATION SCIENCES (2021)
Impact of the COVID-19 outbreak on the US equity sectors: Evidence from quantile return spillovers
Syed Jawad Hussain Shahzad et al.
FINANCIAL INNOVATION (2021)
Estimating posterior inference quality of the relational infinite latent feature model for overlapping community detection
Qianchen Yu et al.
FRONTIERS OF COMPUTER SCIENCE (2020)
Adaptive stock trading strategies with deep reinforcement learning methods
Xing Wu et al.
INFORMATION SCIENCES (2020)
Predicting long-term returns of individual stocks with online reviews
Junran Wu et al.
NEUROCOMPUTING (2020)
Network Representation Learning: A Survey
Daokun Zhang et al.
IEEE TRANSACTIONS ON BIG DATA (2020)
Deep learning for time series classification: a review
Hassan Ismail Fawaz et al.
DATA MINING AND KNOWLEDGE DISCOVERY (2019)
Interconnectedness and systemic risk network of Chinese financial institutions: A LASSO-CoVaR approach
Qifa Xu et al.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS (2019)
An uncertain possibility-probability information fusion method under interval type-2 fuzzy environment and its application in stock selection
Xiuzhi Sang et al.
INFORMATION SCIENCES (2019)
Deep learning-based feature engineering for stock price movement prediction
Wen Long et al.
KNOWLEDGE-BASED SYSTEMS (2019)
Complex network approaches to nonlinear time series analysis
Yong Zou et al.
PHYSICS REPORTS-REVIEW SECTION OF PHYSICS LETTERS (2019)
Predicting catastrophes of non-autonomous networks with visibility graphs and horizontal visibility
Haicheng Zhang et al.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2018)
Does global fear predict fear in BRICS stock markets? Evidence from a Bayesian Graphical Structural VAR model
Elie Bouri et al.
EMERGING MARKETS REVIEW (2018)
Network causality structures among Bitcoin and other financial assets: A directed acyclic graph approach
Qiang Ji et al.
QUARTERLY REVIEW OF ECONOMICS AND FINANCE (2018)
Predicting protein structural classes based on complex networks and recurrence analysis
Mohammad H. Olyaee et al.
JOURNAL OF THEORETICAL BIOLOGY (2016)
Influence maximization in complex networks through optimal percolation
Flaviano Morone et al.
NATURE (2015)
Unraveling chaotic attractors by complex networks and measurements of stock market complexity
Hongduo Cao et al.
CHAOS (2014)
Visibility graph analysis on quarterly macroeconomic series of China based on complex network theory
Na Wang et al.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS (2012)
Recurrence networks-a novel paradigm for nonlinear time series analysis
Reik V. Donner et al.
NEW JOURNAL OF PHYSICS (2010)
Visibility graph approach to exchange rate series
Yue Yang et al.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS (2009)
From time series to complex networks:: The visibility graph
Lucas Lacasa et al.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2008)
Dynamical aspects of interaction networks
G Nicolis et al.
INTERNATIONAL JOURNAL OF BIFURCATION AND CHAOS (2005)
A tutorial on the cross-entropy method
PT De Boer et al.
ANNALS OF OPERATIONS RESEARCH (2005)