Abstract
The forecasting of multi-variate time processes through graph-based techniques has recently been addressed under the graph signal processing framework. However, problems in the representation and the processing arise when each time series carries a vector of quantities rather than a scalar one. To tackle this issue, we devise a new framework and propose new methodologies based on the graph vector autoregressive model. More explicitly, we leverage product graphs to model the high-dimensional graph data and develop multidimensional graph-based vector autoregressive models to forecast future trends with a number of parameters that is independent of the number of time series and a linear computational complexity. Numerical results demonstrating the prediction of moving point clouds corroborate our findings.
Original language | English |
---|---|
Title of host publication | ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) |
Subtitle of host publication | Proceedings |
Publisher | IEEE |
Pages | 5575-5579 |
Number of pages | 5 |
ISBN (Electronic) | 978-1-5090-6631-5 |
ISBN (Print) | 978-1-5090-6632-2 |
DOIs | |
Publication status | Published - 2020 |
Event | ICASSP 2020: IEEE International Conference on Acoustics, Speech and Signal Processing - Barcelona, Spain Duration: 4 May 2020 → 8 May 2020 |
Conference
Conference | ICASSP 2020 |
---|---|
Country/Territory | Spain |
City | Barcelona |
Period | 4/05/20 → 8/05/20 |
Bibliographical note
Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-careOtherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.
Keywords
- Forecasting
- graph signal processing
- product graphs
- time series
- vector autoregressive model