Dynamic Networks with Multi-scale Temporal Structure

12/22/2017
by   Xinyu Kang, et al.
0

We describe a novel method for modeling non-stationary multivariate time series, with time-varying conditional dependencies represented through dynamic networks. Our proposed approach combines traditional multi-scale modeling and network based neighborhood selection, aiming at capturing temporally local structure in the data while maintaining sparsity of the potential interactions. Our multi-scale framework is based on recursive dyadic partitioning, which recursively partitions the temporal axis into finer intervals and allows us to detect local network structural changes at varying temporal resolutions. The dynamic neighborhood selection is achieved through penalized likelihood estimation, where the penalty seeks to limit the number of neighbors used to model the data. We present theoretical and numerical results describing the performance of our method, which is motivated and illustrated using task-based magnetoencephalography (MEG) data in neuroscience.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
07/20/2019

Efficient Bayesian PARCOR Approaches for Dynamic Modeling of Multivariate Time Series

A Bayesian lattice filtering and smoothing approach is proposed for fast...
research
02/22/2020

Bayesian Multi-scale Modeling of Factor Matrix without using Partition Tree

The multi-scale factor models are particularly appealing for analyzing m...
research
05/18/2023

Multi-scale wavelet coherence with its applications

The goal in this paper is to develop a novel statistical approach to cha...
research
05/16/2022

Multi-scale Attention Flow for Probabilistic Time Series Forecasting

The probability prediction of multivariate time series is a notoriously ...
research
01/29/2019

Spectral Multi-scale Community Detection in Temporal Networks with an Application

The analysis of temporal networks has a wide area of applications in a w...
research
05/09/2016

Exact ICL maximization in a non-stationary temporal extension of the stochastic block model for dynamic networks

The stochastic block model (SBM) is a flexible probabilistic tool that c...
research
06/12/2015

Exact ICL maximization in a non-stationary time extension of the latent block model for dynamic networks

The latent block model (LBM) is a flexible probabilistic tool to describ...

Please sign up or login with your details

Forgot password? Click here to reset