SPS Webinar: Multivariate Time Series Forecasting With GARCH Models on Graphs
Date: 22 October 2025
Time: 07:30 AM ET (New York Time)
Presenters: Dr. Ercan E. Kuruoğlu
Based on the IEEE Xplore® article under the same title
Published: IEEE Transactions on Signal and Information Processing over Networks, August 2023.
Download article: Original article will be made publicly available for download on the day of the webinar for 48 hours.
ARTICLE LINK
Abstract
Modern-data provide us various challenges. They are multivariate where different variables relate to each other in an irregular way. Modern-data is also non-stationary, examples including wireless signals over time-varying channels, social networks data, brain signals, etc.
Graph Signal Processing methods have gained important success over the last decade for multivariate data analysis. Various digital signal processing methods have been extended to the graph formulation such as filtering, sampling, followed by the development of the graph versions classical adaptive signal processing methods such as Graph-LMS and Graph-Sign algorithm. The time is mature now for the development of statistical graph-signal processing which models graph signals as stochastic processes. There has already been works in the literature on Graph-Vector AR and Graph- Vector ARMA processes which paved the way for stochastic graph models. Various network data, such as brain connectivity, gene expression networks, wireless communication networks and meteorological networks, change over time and space. To capture this non-stationary characteristics, our presenter will present a family of new graph stochastic models which extend the one-dimensional Generalised Autoregressive Conditional Heteroschedasticity (GARCH) which have been popular in financial data analysis. He will demonstrate the success of the new models on wind-energy forecasting. He hopes that this webinar will initiate discussions on the applications of non-stationary stochastic models various new fields such as neurological signal processing and financial data analysis.
Biography
Ercan E. Kuruoğlu received the Ph.D. degree in information engineering from the University of Cambridge, United Kingdom, in 1998.
He is currently a Full Professor at Tsinghua Shenzhen International Graduate Institute since March 2022. In 1998, he joined Xerox Research Center Europe, Cambridge. He was an ERCIM fellow in 2000 with INRIA-Sophia Antipolis, France. In 2002, he joined ISTI-CNR, Pisa, Italy where he became a Chief Scientist/Director of Research in 2020. He was a Visiting Professor at Tsinghua-Berkeley Shenzhen Institute 2020-2022. His research interests are in the areas of statistical signal and image processing, Bayesian machine learning and information theory with applications in remote sensing, environmental sciences, telecommunications and computational biology.
Dr. Kuruoğlu served as an Associate Editor for the IEEE Transactions on Signal Processing and IEEE Transactions on Image Processing. He was the Editor in Chief of Digital Signal Processing: A Review Journal between 2011-2021. He is currently co-Editor-in-Chief of Journal of the Franklin Institute. He acted as a Technical co-Chair for EUSIPCO 2006 and a Tutorials co-Chair of ICASSP 2014. He is the vice-chair of the IEEE Technical Committee (TC) on Image, Video and Multidimensional Signal Processing and member of TC on Machine Learning for Signal Processing. He is also a member of the IEEE Data Collections and Challenges Committee. He was a plenary speaker at ISSPA 2010, IEEE SIU 2017, Entropy 2018, MIIS 2020, IET IRC 2023 and tutorial speaker at IEEE ICSPCC 2012. He was an Alexander von Humboldt Experienced Research Fellow in the Max Planck Institute for Molecular Genetics in 2013-2015.
