Time series can be found in almost all disciplines nowadays. Thus, time series forecasting is becoming a consolidated discipline that provides meaningful information in a wide variety of application areas, turning their efficient analysis into the utmost relevance for the scientific community. This session pays attention to the extraction of useful knowledge from time series in the context of industrial and environmental applications. The analysis of very large time series, given its relevance in the emergent context of big data, is also encouraged.
Topics of interest for the special session, always in the context of industrial and environmental applications, include but are not limited to:
- Machine learning applied to time series forecasting
- Deep learning applied to time series forecasting
- New approaches for big data time series forecasting
- Hybrid systems for time series analysis
- Ensemble approaches for time series analysis
- Francisco Martínez Álvarez – Pablo de Olavide University of Seville (Spain)
- Luis Domingues – Polytechnic Institute of Beja (Portugal)
- Kristian Sabo – University of Osijek (Croatia)
- Dieu Tien Bui – University of South-Eastern Norway (Norway)
- Isabel S. Brito Sousa – Polytechnic Institute of Beja (Portugal)
- José F. Torres – Pablo de Olavide University of Seville (Spain)
Francisco Martínez Álvarez. Data Science and Big Data Lab, Pablo de Olavide University.