Space-time series forecasting by artificial neural networks

Tao Cheng; Jiaqiu Wang; Xia Li

doi:10.1117/12.816114

29 December 2008 Space-time series forecasting by artificial neural networks

Tao Cheng, Jiaqiu Wang, Xia Li

Proceedings Volume 7285, International Conference on Earth Observation Data Processing and Analysis (ICEODPA); 72853I (2008) https://doi.org/10.1117/12.816114
Event: International Conference on Earth Observation Data Processing and Analysis, 2008, Wuhan, China

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.

Citation Download Citation

Tao Cheng, Jiaqiu Wang, and Xia Li "Space-time series forecasting by artificial neural networks", Proc. SPIE 7285, International Conference on Earth Observation Data Processing and Analysis (ICEODPA), 72853I (29 December 2008); https://doi.org/10.1117/12.816114

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