6 papers
Multiscale Dynamic Dependence Estimation over Networks
Cristian F. Jiménez-Varón, Cristian F. Jiménez-Varón, Marina I. Knight +1
In many settings, observed multivariate time series are often nonstationary in nature, i.e., their second order properties vary over time. An additional feature is that their cross…
Network Time Series Models for Multivariate Volatility Forecasting
Chiara Boetti, Matthew A. Nunes
Realized volatility has become a standard tool for measuring latent variation in financial assets, and its forecasting is crucial for a wide range of financial applications. We pro…
A Doubled Adjacency Spectral Embedding Approach to Graph Clustering
Sinyoung Park, Matthew Nunes, Sandipan Roy
Spectral clustering is a popular tool in network data analysis, with applications in a variety of scientific application areas. However, many studies have shown that classical spec…
Network Estimation for Stationary Time Series
Madeline A. Shelley, Chiara Boetti, Marina I. Knight +1
High-dimensional multivariate time series are common in many scientific and industrial applications, where the interest lies in identifying key dependence structure within the data…
Long memory network time series
Chiara Boetti, Matthew A. Nunes, Marina I. Knight
Many scientific areas, from computer science to the environmental sciences and finance, give rise to multivariate time series which exhibit long memory, or loosely put, a slow deca…
TrendLSW: Trend and Spectral Estimation of Nonstationary Time Series in R
Euan T. McGonigle, Rebecca Killick, Matthew A. Nunes
The TrendLSW R package has been developed to provide users with a suite of wavelet-based techniques to analyse the statistical properties of nonstationary time series. The key comp…