Forescat modeling of spatio-temporal raster data using principal component analysis and a neural networks - wavelet decomposition model - Amao Suxo, Christian (2018)
Nowadays spatio-temporal forecasting has been drawing more and more attention from academia researchers and industrial practitioners for its great utility to plan and develop contingency measures against future adverse conditions. In this thesis, a methodology to forecast maps in spatio- temporal raster datasets is proposed. Following a summarize-predict-and-rebuild methodology, it 1) first suggests a reduction in the dimensionality of data using a principal component analysis, then 2) individual forecasts on the most significant components or eigenvectors are calculated using a neural networks - wavelet decomposition model. Finally, 3) a recursive algorithm, applied on the spectral inverse reconstruction of the individual forecasts, provides the final forecast maps. The devised methodology led to three models according: the spatial principal component anal¬ysis (SPCA) model, the temporal principal component analysis (TPCA) model and the spatio- temporal principal component analysis (STPCA) model. In order to evaluate their forecasting accuracy, a simulation study was carried out by considering datasets with pure temporal, pure spatial and spatio-temporal variability. The results suggest using a TPCA (or SPCA) model when the temporal (or spatial) variability is predominant. For datasets with similar spatial and temporal variability information, the STPCA model provides the best forecast results. The research culmi- nates with a real-world case study in monthly sea surface temperature anomalies of the Tropical Pacific Ocean.