Ricardo de A. Araújo , Paulo S.G. de Mattos Neto , Nadia Nedjah , Sergio C.B. Soares
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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.
期刊介绍:
The journal aims to promote and communicate advances in big data research by providing a fast and high quality forum for researchers, practitioners and policy makers from the very many different communities working on, and with, this topic.
The journal will accept papers on foundational aspects in dealing with big data, as well as papers on specific Platforms and Technologies used to deal with big data. To promote Data Science and interdisciplinary collaboration between fields, and to showcase the benefits of data driven research, papers demonstrating applications of big data in domains as diverse as Geoscience, Social Web, Finance, e-Commerce, Health Care, Environment and Climate, Physics and Astronomy, Chemistry, life sciences and drug discovery, digital libraries and scientific publications, security and government will also be considered. Occasionally the journal may publish whitepapers on policies, standards and best practices.