Data-Driven Spatio-Temporal Modeling Using the Integro-Difference Equation

被引：24

作者：

Dewar, Michael ^{[1
]}

Scerri, Kenneth ^{[2
]}

Kadirkamanathan, Visakan ^{[2
]}

机构：

[1] Univ Edinburgh, Sch Informat, Edinburgh EH8 9AB, Midlothian, Scotland

[2] Univ Sheffield, Dept Automat Control & Syst Engn, Sheffield S1 3JD, S Yorkshire, England

来源：

IEEE TRANSACTIONS ON SIGNAL PROCESSING | 2009年 / 57卷 / 01期

关键词：

Dynamic spatio-temporal modeling; expectation-maximization (EM) algorithm; Integro-Difference Equation (IDE); maximum-likelihood parameter estimation; state-space; MAXIMUM-LIKELIHOOD; IDENTIFICATION; DISPERSAL;

D O I：

10.1109/TSP.2008.2005091

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

A continuous-in-space, discrete-in-time dynamic spatio-temporal model known as the Integro-Difference Equation (IDE) model is presented in the context of data-driven modeling. A novel decomposition of the IDE is derived, leading to state-space representation that does not couple the number of states with the number of observation locations or the number of parameters. Based on this state-space model, an expectation-maximization (EM) algorithm is developed in order to jointly estimate the IDE model's spatial field and spatial mixing kernel. The resulting modeling framework is demonstrated on a set of examples.

引用

页码：83 / 91

页数：9

共 50 条

[31] A novel spatio-temporal trajectory data-driven development approach for autonomous vehicles
Menghan Zhang
Mingjun Ma
Jingying Zhang
Mingzhuo Zhang
Bo Li
Dehui Du
[J]. Frontiers of Earth Science, 2021, 15 : 620 - 630
[32] BSENet: A Data-Driven Spatio-Temporal Representation Learning for Base Station Embedding
Wang, Xinyu
Yang, Tan
Cui, Yidong
Jin, Yuehui
Wang, Hongbo
[J]. IEEE ACCESS, 2020, 8 : 51674 - 51683
[33] A Spatio-Temporal Data-Driven Automatic Control Method for Smart Home Services
Chen, Jinrong
Chen, Zheyi
Zheng, Longhai
Chen, Xing
[J]. COMPANION PROCEEDINGS OF THE WEB CONFERENCE 2022, WWW 2022 COMPANION, 2022, : 948 - 955
[34] Comparing Micromobility with Public Transportation Trips in a Data-Driven Spatio-Temporal Analysis
Schwinger, Felix
Tanriverdi, Baran
Jarke, Matthias
[J]. SUSTAINABILITY, 2022, 14 (14)
[35] Spatio-temporal crime predictions in smart cities: A data-driven approach and experiments
Catlett, Charlie
Cesario, Eugenio
Talia, Domenico
Vinci, Andrea
[J]. PERVASIVE AND MOBILE COMPUTING, 2019, 53 : 62 - 74
[36] Data-Driven Approaches for Spatio-Temporal Analysis: A Survey of the State-of-the-Arts
Monidipa Das
Soumya K. Ghosh
[J]. Journal of Computer Science and Technology, 2020, 35 : 665 - 696
[37] Multitime-Scale Data-Driven Spatio-Temporal Forecast of Photovoltaic Generation
Yang, Chen
Thatte, Anupam A.
Xie, Le
[J]. IEEE TRANSACTIONS ON SUSTAINABLE ENERGY, 2015, 6 (01) : 104 - 112
[38] On a Semiparametric Data-Driven Nonlinear Model with Penalized Spatio-Temporal Lag Interactions
Al-Sulami, Dawlah
Jiang, Zhenyu
Lu, Zudi
Zhu, Jun
[J]. JOURNAL OF TIME SERIES ANALYSIS, 2019, 40 (03) : 327 - 342
[39] Data-driven spatio-temporal analysis of wildfire risk to power systems operation
Umunnakwe, Amarachi
Parvania, Masood
Nguyen, Hieu
Horel, John D.
Davis, Katherine R.
[J]. IET GENERATION TRANSMISSION & DISTRIBUTION, 2022, 16 (13) : 2531 - 2546
[40] Data-driven spatio-temporal dynamic brain connectivity analysis using fALFF: Application to sensorimotor task data
Hossain, Khondoker Murad
Bhinge, Suchita
Long, Qunfang
Calhoun, Vince D.
Adali, Tulay
[J]. 2022 56TH ANNUAL CONFERENCE ON INFORMATION SCIENCES AND SYSTEMS (CISS), 2022, : 200 - 205

← 1 2 3 4 5 →