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Proceedings Paper

Space-time series forecasting by artificial neural networks
Author(s): Tao Cheng; Jiaqiu Wang; Xia Li
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Paper Abstract

Spatio-Temporal Autoregressive Integrated Moving Average (STAIRMA) model family is a very useful tool in modeling space-time series data. It assumes that space-time series data is correlated linearly in space and time. However, in reality most space-time series contains nonlinear space-time autocorrelation structure, which can't be modeled by STARIMA. Artificial neural networks (ANN) have shown great flexibility in modeling and forecasting nonlinear dynamic process. In the paper, we developed an architecture approach to model space-time series data using artificial neural network (ANN). The model is tested with forest fire prediction in Canada. The experimental result demonstrates that STANN achieves much better prediction accuracy than STARIMA model.

Paper Details

Date Published: 29 December 2008
PDF: 8 pages
Proc. SPIE 7285, International Conference on Earth Observation Data Processing and Analysis (ICEODPA), 72853I (29 December 2008); doi: 10.1117/12.816114
Show Author Affiliations
Tao Cheng, Univ. College London (United Kingdom)
Jiaqiu Wang, Sun Yat-sen Univ. (China)
Xia Li, Sun Yat-sen Univ. (China)

Published in SPIE Proceedings Vol. 7285:
International Conference on Earth Observation Data Processing and Analysis (ICEODPA)
Deren Li; Jianya Gong; Huayi Wu, Editor(s)

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