Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/89365
Title: Modeling Spatio-Temporal Extreme Events Using Graphical Models
Authors: Yu, Hang
Dauwels, Justin
Keywords: Extreme Events
Graphical Models
Issue Date: 2016
Source: Yu, H., & Dauwels, J. (2016). Modeling Spatio-Temporal Extreme Events Using Graphical Models. IEEE Transactions on Signal Processing, 64(5), 1101-1116.
Series/Report no.: IEEE Transactions on Signal Processing
Abstract: We propose a novel statistical model to describe spatio-temporal extreme events. The model can be used, for instance, to estimate extreme-value temporal pattern such as seasonality and trend, and further to predict the distribution of extreme events in the future. Such model usually involves thousands or even millions of variables in the spatio-temporal domain, whereas only one single observation is available for each location and time point. To address this challenge, previous works usually employ learning and inference methods that are computationally burdensome, and therefore are prohibitive for large-scale data. Moreover, they assume that the shape and scale parameters of the extreme-value distributions are constant across the spatio-temporal domain, which is often too restrictive in practice. In this paper, we break through these limitations by exploring graphical models to capture the highly structured dependencies among the parameters of extreme-value distributions. Furthermore, we develop an efficient stochastic variational inference (SVI) algorithm to learn the parameters of the resulting non-Gaussian graphical model. The computational complexity of the SVI algorithm is sublinear in the number of variables, thus enabling the proposed model to tackle large-scale spatio-temporal data in real-life applications. Results of both synthetic and real data demonstrate the effectiveness of the proposed approach.
URI: https://hdl.handle.net/10356/89365
http://hdl.handle.net/10220/44847
ISSN: 1053-587X
DOI: 10.1109/TSP.2015.2491882
Rights: © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: [http://dx.doi.org/10.1109/TSP.2015.2491882].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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