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Activity Number: 457 - Novel Statistical Approaches to Time Series of Networks
Type: Topic Contributed
Date/Time: Wednesday, July 31, 2019 : 8:30 AM to 10:20 AM
Sponsor: Section on Nonparametric Statistics
Abstract #302962
Title: Network Granger Causality: Visualization and Extensions
Author(s): Ali Shojaie*
Companies: University of Washington

Multivariate and high-dimensional time series have become ubiquitous in neuroscience, and in many other application domains. The advent of such high-dimensional time series has sparked a new interest in the analysis of multivariate time series data, beyond prediction of future values or events. In the era of “Big Data”, scientists are increasingly interested in inferring the dynamics of complex physical, biological and social systems from massive time series data. In this talk we will focus on reconstructing and visualizing networks from high-dimensional time series, using the concept of Granger causality. We will also discuss extensions of the ‘classical’ framework that allows for non-linearity, non-stationarity, and subsampling of time series, and demonstrate their use by analyzing data from various brain imaging modalities.

Authors who are presenting talks have a * after their name.

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