On the Sea Surface Temperature Forecasting Problem with Deep Dilation-Erosion-Linear Models

The sea surface temperature (SST) is considered an important measure for detecting changes in climate and marine ecosystems. So, its forecasting is essential for supporting governmental strategies to avoid side effects on the global population. In this paper, we analyze the SST time series and suggest that a combination between a linear component and a nonlinear component with long-term dependency can better represent it. Based on this assumption, we propose a deep neural network architecture with dilation-erosion-linear (DEL) processing units to deal with this particular kind of time series. An empirical analysis is performed in this work using three SST time series, where we explore three statistical measures. The experimental results demonstrate that the proposed model outperformed recent and classical literature forecasting techniques according to well-known performance metrics.