Show simple item record

dc.contributor.authorNatali, Alberto
dc.contributor.authorIsufi, Elvin
dc.contributor.authorLeus, Geert
dc.date.accessioned2020-09-21T07:08:43Z
dc.date.available2020-09-21T07:08:43Z
dc.date.issued2020-04-09
dc.identifier.citationNatali, A., Isufi, E., & Leus, G. (2020). Forecasting Multi-Dimensional Processes Over Graphs. ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). doi:10.1109/icassp40776.2020.9053522
dc.identifier.isbn9781509066315
dc.identifier.issn1520-6149
dc.identifier.doi10.1109/icassp40776.2020.9053522
dc.identifier.urihttp://hdl.handle.net/10754/665249
dc.description.abstractThe 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.
dc.description.sponsorshipThis work was supported in parts by the KAUST-MIT-TUD consortium grant OSR-2015-Sensors-2700.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.urlhttps://ieeexplore.ieee.org/document/9053522/
dc.rightsArchived with thanks to IEEE
dc.titleForecasting Multi-Dimensional Processes Over Graphs
dc.typeConference Paper
dc.conference.date2020-05-04 to 2020-05-08
dc.conference.name2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020
dc.conference.locationBarcelona, ESP
dc.eprint.versionPost-print
dc.contributor.institutionMathematics and Computer Science Delft University of Technology,Faculty of Electrical Engineering,Delft,The Netherlands
dc.identifier.volume2020-May
dc.identifier.pages5575-5579
dc.identifier.arxivid2004.08260
kaust.grant.numberOSR-2015-Sensors-2700
dc.identifier.eid2-s2.0-85089225121
kaust.acknowledged.supportUnitOSR-2015-Sensors-2700
dc.date.published-online2020-04-09
dc.date.published-print2020-05
dc.date.posted2020-04-17


This item appears in the following Collection(s)

Show simple item record