Fusion of deep learning models for temporal data to obtain more effective and efficient models to be applied in real time
Machine learning techniques for earthquakes and environmental pollution time series forecasting.
Development of machine learning models for massive and time-indexed data analysis.
Analysis and development of predictive models applied to forecasting of relevant variables in the electric market.
Support and development of data analytics systems to help making decisions for water supply and urban consumption scenarios.
Research and development of complex machine learning algorithms to provide accurate predictions for the evolution of the olive fly pest agent.