Research interests

Main research line: time series forecasting with computational intelligence models

Many scientific fields produce time-ordered data, which are usually not independent from one another. Great benefits (not only economic) can be obtained on these scientific fields by analysing these data, and particularly by properly forecasting them.

Some applications developed during the last years:

• Airborne pollen: from historical records and from meteorological measures and predictions, forecasting the concentrations of pollen of different species.
• Air quality: also from historical records, meteorological measures and predictions and other values as traffic, the idea is to analyse, model and forecast the concentrations of different pollutants in the air.
• Health related series: forecasting the number of hospital admissions from pollution and other variables.
• Earthquakes: from historic earthquake series, the discovery of precursor events helps forecasting the occurrence of potentially hazardous earthquakes.

Other research lines include the application of computational intelligence to solve other problems including dynamic electrical line rating, the detection and identification of pollen grains in images, participatory and deliberative democracy and others.