Investigation of Stationarity for Graph Time Series Data Sets

Date

2021-01-07

Author

Güneyi, Eylem Tuğçe
Vural, Elif

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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Graphs permit the analysis of the relationships in complex data sets effectively. Stationarity is a feature that facilitates the analysis and processing of random time signals. Since graphs have an irregular structure, the definition of classical stationarity does not apply to graphs. In this study, we study how stationarity is defined for graph random processes and examine the validity of the stationarity assumption with experiments on synthetic and real data sets.

Subject Keywords

Graph signal processing, stationarity, time-vertex processes

URI

https://hdl.handle.net/11511/89406

DOI

https://doi.org/10.1109/siu49456.2020.9302376

Conference Name

2020 28th Signal Processing and Communications Applications Conference (SIU)

Collections

Department of Electrical and Electronics Engineering, Conference / Seminar

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Citation Formats

E. T. Güneyi and E. Vural, “Investigation of Stationarity for Graph Time Series Data Sets,” presented at the 2020 28th Signal Processing and Communications Applications Conference (SIU), 2021, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/89406.