January 13, 2023 Miguel Atencia

SS09

SS09: Deep Learning and Time Series Forecasting: Methods and Applications

Francisco Martínez Álvarez

Universidad Pablo de Olavide, Spain

Verónica Bolón Canedo

Universidade da Coruña, Spain

David Camacho

Universidad Politécnica de Madrid, Spain

Abstract

On the one hand, 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 areas, turning their efficient analysis into the utmost relevance for the scientific community.

On the other hand, deep learning has become one of the most powerful and successful strategies to extract information from large datasets. Many researchers are now developing their models based on deep learning, being applied to almost all disciplines as well.

This session pays attention to the extraction of useful knowledge from time series with deep learning. The analysis of very large time series, given its relevance in the emergent context of big data, is also encouraged. The submission of papers including both novel methods and relevant applications is encouraged.

Topics of interest for the special session include but are not limited to:

  1. New approaches based on deep learning for big data time series forecasting.
  2. Hybrid deep learning systems for time series forecasting.
  3. Ensemble deep learning approaches for time series forecasting.
  4. New explainable artificial intelligence models for time series forecasting with deep learning.
  5. Deep transfer learning for time series forecasting.
  6. Application of deep learning to sound real-world problems.

Organizer

Dr. Francisco Martínez Alvárez‘s most remarkable achievements have been done in the field of Artificial Intelligence, Data Mining and Big Data and, obviously, Deep Learning with a great diversity of tools and algorithms developed in different areas: classification, pre-processing, regression and, especially, in the prediction of time series. Such algorithms have been successfully applied to different fields like smart grids, environment, remote sensing and seismic engineering. An important characteristic of all the research he has carried out is that it has been continuously applied to real problems in society. Thus, problems as diverse as the prediction of natural disasters, air pollution, remote sensing or renewable energies have been addressed. In addition, most of the work has been done in close collaboration with the private sector and, therefore, the transfer of knowledge to industry has been ensured thanks to the software and actions developed.

Dr. Verónica Bolón-Canedo has been working on artificial intelligence and machine learning since 2009. After a postdoctoral fellowship in the University of Manchester, UK (2015), she is currently an Associate Professor in the Department of Computer Science and Information Technologies of the University of A Coruña. She has co-authored two books, seven book chapters, and more than 100 research papers in international conferences and journals. She co-organized several special sessions at international conferences, such as ESANN and IJCNN, and served in program and scientific committees. Her current research interests include machine learning, deep learning and big data. She has served as Secretary of the Spanish Association of Artificial Intelligence and is member of the Spanish Young Academy and the Royal Academy of Sciences of Spain.

Dr. David Camacho is full professor at Computer Systems Engineering Department of Universidad Politécnica de Madrid (UPM), and the head of the Applied Intelligence and Data Analysis research group (AIDA: https://aida.etsisi.uam.es) at UPM. He holds a Ph.D. in Computer Science from Universidad Carlos III de Madrid in 2001 with honors (best thesis award in Computer Science). He has published more than 300 journals, books, and conference papers. His research interests include Machine Learning (Clustering/Deep Learning), Computational Intelligence (Evolutionary Computation, Swarm Intelligence), Social Network Analysis, Fake News and Disinformation Analysis. He has participated/led more than 50 research projects (National and European: H2020, DG Justice, ISFP, and Erasmus+), regarding to the design and application of artificial intelligence methods for data mining and optimization for problems emerging in industrial scenarios (coal mining, steel), aeronautics, aerospace engineering, cybercrime/cyber intelligence, social networks applications, or video games among others. He serves as Editor in Chief of Expert Sytems from 2023, and he is an Associate Editor of several journals including Information Fusion and Cognitive Computation.