Modeling Spatio-Temporal Extreme Events Using Graphical Models
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 thousan...
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| Format: | Journal Article |
| Language: | English |
| Published: |
2018
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| Online Access: | https://hdl.handle.net/10356/89365 http://hdl.handle.net/10220/44847 |
| _version_ | 1826128825085329408 |
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| author | Yu, Hang Dauwels, Justin |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering Yu, Hang Dauwels, Justin |
| author_sort | Yu, Hang |
| collection | NTU |
| description | 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. |
| first_indexed | 2024-10-01T07:30:53Z |
| format | Journal Article |
| id | ntu-10356/89365 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T07:30:53Z |
| publishDate | 2018 |
| record_format | dspace |
| spelling | ntu-10356/893652020-03-07T14:02:37Z Modeling Spatio-Temporal Extreme Events Using Graphical Models Yu, Hang Dauwels, Justin School of Electrical and Electronic Engineering Extreme Events Graphical Models 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. MOE (Min. of Education, S’pore) Accepted version 2018-05-21T08:11:27Z 2019-12-06T17:23:55Z 2018-05-21T08:11:27Z 2019-12-06T17:23:55Z 2016 Journal Article Yu, H., & Dauwels, J. (2016). Modeling Spatio-Temporal Extreme Events Using Graphical Models. IEEE Transactions on Signal Processing, 64(5), 1101-1116. 1053-587X https://hdl.handle.net/10356/89365 http://hdl.handle.net/10220/44847 10.1109/TSP.2015.2491882 en IEEE Transactions on Signal Processing © 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]. 16 p. application/pdf |
| spellingShingle | Extreme Events Graphical Models Yu, Hang Dauwels, Justin Modeling Spatio-Temporal Extreme Events Using Graphical Models |
| title | Modeling Spatio-Temporal Extreme Events Using Graphical Models |
| title_full | Modeling Spatio-Temporal Extreme Events Using Graphical Models |
| title_fullStr | Modeling Spatio-Temporal Extreme Events Using Graphical Models |
| title_full_unstemmed | Modeling Spatio-Temporal Extreme Events Using Graphical Models |
| title_short | Modeling Spatio-Temporal Extreme Events Using Graphical Models |
| title_sort | modeling spatio temporal extreme events using graphical models |
| topic | Extreme Events Graphical Models |
| url | https://hdl.handle.net/10356/89365 http://hdl.handle.net/10220/44847 |
| work_keys_str_mv | AT yuhang modelingspatiotemporalextremeeventsusinggraphicalmodels AT dauwelsjustin modelingspatiotemporalextremeeventsusinggraphicalmodels |