Online Topology Identification from Vector Autoregressive Time Series
| العنوان: | Online Topology Identification from Vector Autoregressive Time Series |
|---|---|
| المؤلفون: | Daniel Romero, Bakht Zaman, Luis Miguel Lopez Ramos, Baltasar Beferull-Lozano |
| سنة النشر: | 2019 |
| مصطلحات موضوعية: | Signal Processing (eess.SP), FOS: Computer and information sciences, Theoretical computer science, Computer science, Estimator, Machine Learning (stat.ML), 020206 networking & telecommunications, Regret, 02 engineering and technology, Causality, Synthetic data, Causality (physics), Autoregressive model, Granger causality, Statistics - Machine Learning, Signal Processing, FOS: Electrical engineering, electronic engineering, information engineering, 0202 electrical engineering, electronic engineering, information engineering, Anomaly detection, Electrical and Electronic Engineering, Time series, Electrical Engineering and Systems Science - Signal Processing |
| الوصف: | Causality graphs are routinely estimated in social sciences, natural sciences, and engineering due to their capacity to efficiently represent the spatiotemporal structure of multivariate data sets in a format amenable for human interpretation, forecasting, and anomaly detection. A popular approach to mathematically formalize causality is based on vector autoregressive (VAR) models and constitutes an alternative to the well-known, yet usually intractable, Granger causality. Relying on such a VAR causality notion, this paper develops two algorithms with complementary benefits to track time-varying causality graphs in an online fashion. Their constant complexity per update also renders these algorithms appealing for big-data scenarios. Despite using data sequentially, both algorithms are shown to asymptotically attain the same average performance as a batch estimator which uses the entire data set at once. To this end, sublinear (static) regret bounds are established. Performance is also characterized in time-varying setups by means of dynamic regret analysis. Numerical results with real and synthetic data further support the merits of the proposed algorithms in static and dynamic scenarios. 23 pages including supplementary material, submitted to IEEE Transactions on Signal Processing |
| اللغة: | English |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::443f5725405fbfe03153e18cc2a46d21 http://arxiv.org/abs/1904.01864 |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....443f5725405fbfe03153e18cc2a46d21 |
| قاعدة البيانات: | OpenAIRE |
| الوصف غير متاح. |