scholarly journals Evaluation of Granger Causality Measures for Constructing Networks from Multivariate Time Series

Entropy ◽  
2019 ◽  
Vol 21 (11) ◽  
pp. 1080 ◽  
Author(s):  
Elsa Siggiridou ◽  
Christos Koutlis ◽  
Alkiviadis Tsimpiris ◽  
Dimitris Kugiumtzis
Keyword(s):  
Time Series ◽  
Dynamical Systems ◽  
Financial Markets ◽  
Dimension Reduction ◽  
Granger Causality ◽  
Multivariate Time Series ◽  
High Dimensional ◽  
Information Measures ◽  
Model Based ◽  
The Time Domain

Granger causality and variants of this concept allow the study of complex dynamical systems as networks constructed from multivariate time series. In this work, a large number of Granger causality measures used to form causality networks from multivariate time series are assessed. These measures are in the time domain, such as model-based and information measures, the frequency domain, and the phase domain. The study aims also to compare bivariate and multivariate measures, linear and nonlinear measures, as well as the use of dimension reduction in linear model-based measures and information measures. The latter is particular relevant in the study of high-dimensional time series. For the performance of the multivariate causality measures, low and high dimensional coupled dynamical systems are considered in discrete and continuous time, as well as deterministic and stochastic. The measures are evaluated and ranked according to their ability to provide causality networks that match the original coupling structure. The simulation study concludes that the Granger causality measures using dimension reduction are superior and should be preferred particularly in studies involving many observed variables, such as multi-channel electroencephalograms and financial markets.

scholarly journals Volatility Similarity and Spillover Effects in G20 Stock Market Comovements: An ICA-Based ARMA-APARCH-M Approach

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-18
Author(s):  
Shanglei Chai ◽  
Zhen Zhang ◽  
Mo Du ◽  
Lei Jiang
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Financial Markets ◽  
Stock Markets ◽  
Time Series Data ◽  
Multivariate Time Series ◽  
Spillover Effects ◽  
Series Data ◽  
Dominant Source ◽  
Financial Internationalization

Financial internationalization leads to similar fluctuations and spillover effects in financial markets around the world, resulting in cross-border financial risks. This study examines comovements across G20 international stock markets while considering the volatility similarity and spillover effects. We provide a new approach using an ICA- (independent component analysis-) based ARMA-APARCH-M model to shed light on whether there are spillover effects among G20 stock markets with similar dynamics. Specifically, we first identify which G20 stock markets have similar volatility features using a fuzzy C-means time series clustering method and then investigate the dominant source of volatility spillovers using the ICA-based ARMA-APARCH-M model. The evidence has shown that the ICA method can more accurately capture market comovements with nonnormal distributions of the financial time series data by transforming the multivariate time series into statistically independent components (ICs). Our findings indicate that the G20 stock markets are clustered into three categories according to volatility similarity. There are spillover effects in stock market comovements of each group and the dominant source can be identified. This study has important implications for investors in international financial markets and for policymakers in G20 countries.

scholarly journals Interactions of viral pathogens on hospital admissions for pneumonia, croup and chronic obstructive pulmonary diseases: results of a multivariate time-series analysis

2006 ◽  
Vol 134 (6) ◽  
pp. 1174-1178 ◽  
Author(s):  
R. E. G. UPSHUR ◽  
R. MOINEDDIN ◽  
E. J. CRIGHTON ◽  
M. MAMDANI
Keyword(s):  
Time Series ◽  
Granger Causality ◽  
Hospital Admissions ◽  
Multivariate Time Series ◽  
Chronic Obstructive Lung Disease ◽  
Epidemiological Data ◽  
Chronic Obstructive ◽  
Causality Test ◽  
Syncytial Virus ◽  
The Impact

Co-circulation of respiratory syncytial virus (RSV) and influenza has made the partitioning of morbidity and mortality from each virus difficult. Given the interaction between chronic obstructive lung disease (COPD) and pneumonia, often one can be mistaken for the other. Multivariate time-series methodology was applied to examine the impact of RSV and influenza on hospital admissions for bronchiolitis, pneumonia, and COPD. The Granger Causality Test, used to determine the causal relationship among series, showed that COPD and pneumonia are not influenced by RSV (P=0·2999 and 0·7725), but RSV does influence bronchiolitis (P=0·0001). Influenza was found to influence COPD, pneumonia, and bronchiolitis (P<0·0001). The use of multivariate time series and Granger causality applied to epidemiological data clearly illustrates the significant contribution of influenza and RSV to morbidity in the population.

scholarly journals High-Dimensional Multivariate Time Series With Additional Structure

2017 ◽  
Vol 26 (3) ◽  
pp. 610-622 ◽  
Author(s):  
Michael Schweinberger ◽  
Sergii Babkin ◽  
Katherine B. Ensor
Keyword(s):  
Time Series ◽  
Multivariate Time Series ◽  
High Dimensional ◽  
Additional Structure

scholarly journals Detecting brain connectivity changes during seizures using Granger causality measures on scalp EEG

2015 ◽  
Author(s):  
Christos Koutlis
Keyword(s):  
Time Series ◽  
Granger Causality ◽  
Time Window ◽  
Brain Connectivity ◽  
Characteristic Curve ◽  
Multivariate Time Series ◽  
Brain Regions ◽  
Brain Network ◽  
Network Feature ◽  
The Brain

In this work the objective is to detect brain connectivity changes during epileptic seizures using methods of multivariate time series analysis on scalp multi-channel EEG. Different brain regions represented by the electrode positions interact in terms of Granger causality and these directed connections formulate the brain network at a certain time window. The numerous proposed network features are believed to capture the information of many network characteristics. The ability of a single network feature of the brain network to detect the transition of brain activity from preictal to ictal is examined. The connectivity of the brain is estimated by 13 Granger causality indices on 7 epochs from multivariate time series (19 channels per epoch) at 15 time windows of 20 seconds (5 min in total) before seizure and during the seizure. The characteristics of the networks are estimated by 379 network features. Finally, the discrimination task (preictal vs. ictal) for each network feature is evaluated by the area under receiver operating characteristic curve (AUROC).

Clustering and Visualization of Multivariate Time Series

2010 ◽  
pp. 176-194
Author(s):  
Alfredo Vellido ◽  
Iván Olier
Keyword(s):  
Probability Theory ◽  
Time Series ◽  
Bayesian Approach ◽  
Multivariate Time Series ◽  
Unsupervised Clustering ◽  
High Dimensional ◽  
Topographic Mapping ◽  
Generative Topographic Mapping ◽  
Exploratory Investigation ◽  
High Dimensional Datasets

The exploratory investigation of multivariate time series (MTS) may become extremely difficult, if not impossible, for high dimensional datasets. Paradoxically, to date, little research has been conducted on the exploration of MTS through unsupervised clustering and visualization. In this chapter, the authors describe generative topographic mapping through time (GTM-TT), a model with foundations in probability theory that performs such tasks. The standard version of this model has several limitations that limit its applicability. Here, the authors reformulate it within a Bayesian approach using variational techniques. The resulting variational Bayesian GTM-TT, described in some detail, is shown to behave very robustly in the presence of noise in the MTS, helping to avert the problem of data overfitting.

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