Graph-Time Trend Filtering and Unrolling Network

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review


Reconstructing missing values and removing noise from network-based multivariate time series requires developing graph-time regularizers capable of capturing their spatiotemporal behavior. However, current approaches based on joint spatiotemporal smoothness, diffusion, or variations thereof may not be effective for time series with discontinuities across the graph or time. To address this challenge, we propose a joint graph-time trend filter operating over a product graph representing spatiotemporal relations. Additionally, we develop a graph-time unrolled neural network to learn the prior from the data, which is based on the alternating direction method of multipliers iterations of the graph-time trend filter and on graph-time convolutional filters. Numerical tests with two synthetic and four real datasets corroborate the effectiveness of both approaches, highlight their inherent trade-offs, and show they compare well with state-of-the-art alternatives.
Original languageEnglish
Title of host publicationProceedings of the 2023 31st European Signal Processing Conference (EUSIPCO)
Number of pages5
ISBN (Electronic)978-9-4645-9360-0
ISBN (Print)979-8-3503-2811-0
Publication statusPublished - 2023
Event31st European Signal Processing Conference - Helsinki, Finland
Duration: 4 Sept 20238 Sept 2023
Conference number: 31

Publication series

NameEuropean Signal Processing Conference
ISSN (Print)2219-5491
ISSN (Electronic)2076-1465


Conference31st European Signal Processing Conference
Abbreviated titleEUSIPCO 2023

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project
Otherwise 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.


  • Graph-time signal processing
  • graph unrolled networks
  • trend filtering on graphs


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