Interpolation of Multifeature Time-Vertex Signals via Joint Spectral ARMA Model Çok Boyutlu Zaman-Çizge Sinyalleri ?Için Ortak Spektral ARMA Modeli ile Eksik Veri Kestirimi
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/siu71813.2026.11636888
- Basıldığı Şehir: İstanbul
- Basıldığı Ülke: Türkiye
- Anahtar Kelimeler: ARMA Model, Joint Spectral Analysis, Multifeature Signals, Signal Processing, Time-Vertex Signals
- Orta Doğu Teknik Üniversitesi Adresli: Evet
Özet
Graph signal processing provides effective techniques for the analysis of data structures residing on irregular topologies. Existing time-vertex signal processing approaches in the literature generally focus on single-attribute signals. However, many applications require working with data types where multiple attributes (e.g., temperature, humidity, wind speed) are measured simultaneously at each node. In this study, a novel Multi-feature Joint Spectral ARMA (MF-JS-ARMA) model based on the Joint Wide Sense Stationarity assumption is proposed for modeling multi-attribute time-vertex signals and estimating missing observations in partially observed data. The proposed method simultaneously learns the auto-spectral and cross-spectral densities of attribute pairs within the framework of a joint optimization problem. The developed model has been applied to the LMMSE-based missing data estimation problem on meteorological data, and it has been shown to improve estimation performance compared to reference methods in the literature.