Edge-indexed network time series with graph Ornstein-Uhlenbeck dynamics
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , |
|---|---|
| Format: | Preprint |
| Publié: |
2026
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866916016089464832 |
|---|---|
| author | Chen, Jiaming Veraart, Almut E. D. |
| author_facet | Chen, Jiaming Veraart, Almut E. D. |
| contents | We introduce a class of Lévy-driven graph Ornstein-Uhlenbeck (grOU) models for edge-indexed network time series. The proposed framework extends generalized network autoregressive (GNAR) processes for edge-indexed network time series to continuous time and adapts graph Ornstein-Uhlenbeck dynamics, originally developed for node-indexed processes, to the edge-indexed setting. The model accommodates general Lévy noise and therefore captures both Brownian and jump behavior. We show that the model parameters can be estimated via a maximum-likelihood framework and derive the asymptotic properties of the estimator. We examine the finite-sample performance of the methodology through simulation studies and illustrate its practical relevance in an empirical application to high-frequency financial data. The results indicate that grOU models for edge-indexed network time series improve forecasting accuracy and reduce computational time relative to standard benchmarks while maintaining robustness through their network-based parametrization. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_15907 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Edge-indexed network time series with graph Ornstein-Uhlenbeck dynamics Chen, Jiaming Veraart, Almut E. D. Statistics Theory 62M10, 60G10, 60G51, 62F12, 60E07 We introduce a class of Lévy-driven graph Ornstein-Uhlenbeck (grOU) models for edge-indexed network time series. The proposed framework extends generalized network autoregressive (GNAR) processes for edge-indexed network time series to continuous time and adapts graph Ornstein-Uhlenbeck dynamics, originally developed for node-indexed processes, to the edge-indexed setting. The model accommodates general Lévy noise and therefore captures both Brownian and jump behavior. We show that the model parameters can be estimated via a maximum-likelihood framework and derive the asymptotic properties of the estimator. We examine the finite-sample performance of the methodology through simulation studies and illustrate its practical relevance in an empirical application to high-frequency financial data. The results indicate that grOU models for edge-indexed network time series improve forecasting accuracy and reduce computational time relative to standard benchmarks while maintaining robustness through their network-based parametrization. |
| title | Edge-indexed network time series with graph Ornstein-Uhlenbeck dynamics |
| topic | Statistics Theory 62M10, 60G10, 60G51, 62F12, 60E07 |
| url | https://arxiv.org/abs/2605.15907 |