FUZZ-IEEE 2020 – International Conference on Fuzzy Systems

Special Session on Fuzzy Time Series

FUZZ-IEEE 2020 – International Conference on Fuzzy Systems

Link FUZZ-IEEE 2020: https://wcci2020.org/


Song and Chissom introduced Fuzzy Time Series (FTS) in 1993 to deal with vague and imprecise knowledge in time series data. FTS methods have been drawing more attention and relevance in recent years due to many studies reporting its good accuracy compared with other models. FTS forecasting methods produce data-driven and non-parametric models and have become attractive due to their simplicity, versatility, forecasting accuracy, and computational performance, and it also produces human-readable representations of the time series patterns, making its knowledge transferable, auditable, easily reusable and updatable. Examples of successful applications are shown in energy load forecasting, solar and wind forecasting, stock index prices prediction, seasonal time series, interval forecasting, and probabilistic forecasting.

More recently, many sophisticated and hybrid models have appeared in the literature, showing remarkable results and correcting some methodological problems found in previous studies. The interesting aspect of FTS models is to identify patterns and causal relations in the temporal sequence of fuzzy sets that represent the fuzzified historical data. These temporal patterns generate fuzzy rules, which are then used for prediction.

The goal of the special session is to provide a broad overview of the recent developments in fuzzy time series methods and applications and to explore research challenges in specific topics as detailed below.

Scope and Topics:

  • Multi-variate time series forecasting
  • Spatio-temporal models
  • Techniques for high-order, trend and seasonality
  • Type-2 fuzzy time series
  • Techniques to address nonstationary data and concept drifts
  • Data stream forecasting
  • Rule mining for fuzzy time series
  • Split-and-merge techniques for fuzzy rules
  • Symbolic time series
  • Interval data and interval forecasting
  • Probabilistic forecasting
  • Fuzzy linguistic data in time series
  • Hybrid methods and neuro-fuzzy approaches
  • Evolutionary algorithms for fuzzy time series
  • Hyper-parameter tuning and adaptation
  • Big data time series
  • Distributed, parallel algorithms and scalability issues
  • Applications in energy: load, solar and wind forecasting
  • Financial time series, stock markets, foreign exchange
  • Sentiment analysis and opinion time series
  • Fault detection and prediction
  • Predictive maintenance
  • Applications in industry


Prof. Frederico Gadelha Guimarães – Federal University of Minas Gerais (UFMG), Brazil. E-mail: fredericoguimaraes@ufmg.br
Prof. Muhammad Hisyam Lee – University Technology Malaysia (UTM), Malaysia. E-mail: mhl@utm.my
Dr. Hossein Javedani Sadaei – Chief Digital Officer (CDO) in Gotodata, Brazil. E-mail: h.javedani@gmail.com
Dr. Petrônio Cândido de Lima e Silva – Federal Institute of Northern Minas Gerais (IFNMG), Brazil. E-mail: petronio.candido@ifnmg.edu.br


Frederico G. Guimarães (SM’19) received the B.Eng., M.Sc., and Ph.D. degrees in electrical engineering from the Federal University of Minas Gerais (UFMG), Belo Horizonte, Brazil, in 2003, 2004, and 2008, respectively. He also had a post-doctoral fellowship at the Laboratoire Images, Signaux et Systèmes Intelligents (LiSSi), Université Paris-Est Créteil (UPEC), Paris, France, from 2017 to 2018. In 2010, he joined the Department of Electrical Engineering, UFMG, and in 2018, he became an Associate Professor. He has been responsible for the Machine Intelligence and Data Science Laboratory (MINDS) for computational intelligence research since 2014. He has published more than 200 articles in journals, congresses, and chapters of national and international books. He has experience in electrical engineering and computer engineering, with an emphasis on optimization, computational intelligence, genetic algorithms, and evolutionary computation. He is also a senior member of the IEEE Computational Intelligence Society (CIS) and the IEEE Systems, Man, and Cybernetics Society (SMCS). He received the One-Year Visiting Student Scholarship at McGill University, Montreal, Canada, from 2006 to 2007.

Muhammad H. Lee holds the degree in B.Sc. (1991), M.Sc. (1994) in Statistics of Universiti Kebangsaan Malaysia and Ph.D (2003) in Mathematics of Universiti Teknologi Malaysia. He has been the faculty since 1991 and currently serving as Professor of the Department of Mathematical Sciences, Universiti Teknologi Malaysia (UTM). Currently, he is a member of University Senate, UTM. Previously, served in Standing Committees on Academic Affairs, Research & Innovation, Library & Educational Resources, and Industry & Community Linkages of UTM. Also, served as Manager (Information Technology), Office of the Deputy Vice-Chancellor (Academic and International), UTM during Apr. 2014 – Feb. 2018 and as Manager (Information Technology), Research Management Centre, UTM during Nov. 2010 – Nov 2012. Currently, working as Deputy Director (Analytics and Institutional Research), Office of Strategy Management, Office of the Vice-Chancellor, UTM. He has more than 25 years of teaching and research experience. His interest includes academic matters, academic programme assessment and review, research assessment exercise, statistical science, and information technology. He has contributed more that 100 research papers which are published in reputed journals or conferences.

Hossein J. Sadaei received his B.Sc. in applied mathematics in 2001. He received his M.Sc. degree in pure mathematics from Zanjan University, Iran in 2004. He has also received his Ph.D. degree from Universiti Teknologi Malaysia in 2013, in Statistics. Starting from 2014, he was accepted as a postdoctoral fellow in UFMG, Brazil. His research interests include fuzzy time series, forecasting, learning systems, optimization algorithms, big data, natural language processing. Dr. Javedani has more than 10 years of experience in developing advanced forecasting methods and is the author of more than 20 scientific publications and few patents. Most of his efforts are about combining fuzzy time series methods with novel concepts and deep learning to improve the performance of forecasting. He is conducting a new concept in the fuzzy time series, namely, polynomial fuzzy time series. Following a Ph.D. in Statistics, he took up a position as leader analytics of the big data team in the research and development center of Telecom Malaysia. Dr. Javedani is currently a Chief Digital Officer (CDO) in Gotodata and also an active member of MINDS lab at UFMG.

Petrônio C. Lima e Silva was born in Janauba, Brazil, in 1982. He received the bachelor degree in Information Systems from the Santo Agostinho Faculty of Exact and Technological Sciences (FACET, Montes Claros, Brazil), in 2005, the M.Sc. degree in Informatics from the Pontifical Catholic University of Minas Gerais (PUC/MG, Belo Horizonte, Brazil), in 2010, and the Ph.D. in electrical engineering from the Federal University of Minas Gerais (UFMG), Brazil, in 2019. In 2013 he joined the Federal Institute of Northern Minas Gerais (IFNMG, Januaria, Brazil) working as lecturer in undergraduate courses in software development, databases, data-ware-housing and machine intelligence. His current research interests include data science methods for spatio-temporal data, computational intelligence and machine learning, including scalable soft computing methods for probabilistic forecasting of dynamic and complex systems. He got his Ph.D. in Electrical Engineering at the Machine Intelligence and Data Science (MINDS) laboratory at the Federal University of Minas Gerais (Belo Horizonte, Brazil) and is also member of the Research Group on Data Science and Computational Intelligence - CIDIC, at IFNMG, Brazil.