Local Stationarity in Time-Varying Graph Signals Zaman-Dü?güm Sinyallerinde Yerel Dura?ganlik8/30/2026 5:54:27 PM
34th Signal Processing and Communications Applications Conference, SIU 2026, İstanbul, Türkiye, 7 - 10 Temmuz 2026, (Tam Metin Bildiri)
- Yayın Türü: Bildiri / Tam Metin Bildiri
- Doi Numarası: 10.1109/siu71813.2026.11636427
- Basıldığı Şehir: İstanbul
- Basıldığı Ülke: Türkiye
- Anahtar Kelimeler: Graph signal processing, local stationarity, moving average processes, timevertex processes
- Orta Doğu Teknik Üniversitesi Adresli: Evet
Özet
Graph signal processing provides a powerful framework for analyzing data defined over irregular network structures. Estimation of effective models from a set of timevarying graph signals requires capturing both temporal dynamics and graph-dependent statistical structures. Existing approaches that model time-vertex signals as stochastic processes typically assume a globally stationary model, which often fails to represent local variations that naturally arise across both temporal and graph dimensions. In this work, we address the problem of learning parametric models for graph signals exhibiting locally stationary behavior over time and graph. We propose a locally stationary time-vertex signal model that extends stationarity to a locally adaptive setting and develop an algorithm to learn the model parameters. Experiments on synthetic and real datasets demonstrate improved estimation accuracy over existing time-vertex methods.