Forecasting Models for Graph Processes

Forecasting Models for Graph Processes: A Study on the Multi-Dimensional Case

van der Hoeven, Jelmer (TU Delft Electrical Engineering, Mathematics and Computer Science)

Leus, G.J.T. (mentor)
Natali, A. (mentor)

Delft University of Technology

Electrical Engineering

2022-02-21

In the current Big Data era, large amounts of data are collected from complex systems, such as sensor networks and social networks. The emerging field of graph signal processing (GSP) leverages a network structure (graph) to process signals on an irregular domain. This thesis studies the forecasting of multi-dimensional graph processes, i.e., where each entity in the network carries a multivariate time series. Recent research has proposed to use product graphs to model the dependencies between different variables in multi-dimensional graph processes and employ them in graph-based vector autoregressive models to predict future values. A problem with these product graph-based models is that they can be too restrictive. In this work, it is proposed to combine product graph-based models with multiple one-dimensional models to implement more estimation flexibility. To further increase the degrees of freedom, the use of multiple-input-multiple-output graph filters is also proposed. The proposed models are implemented and tested on synthetic and real-world data sets, which shows an improved forecasting performance compared to state-of-the-art alternatives.

Forecasting
Graph signal processing
Multi-dimensional signal processing
product graphs

http://resolver.tudelft.nl/uuid:325997ff-14c6-43c7-a1ba-cff891817872

Student theses

master thesis

© 2022 Jelmer van der Hoeven