Guest Editorial for Special Issue on Time Series Classification

Houshang Darabi (S’98–A’00–M’10–SM’14) received the Ph.D. degree in industrial and systems engineering from Rutgers University, New Brunswick, NJ, USA, in 2000. He is currently an Associate Professor with the Department of Mechanical and Industrial Engineering, University of Illinois at Chicago (UIC), and also an Associate Professor with the Department of Computer Science, UIC. His research has been supported by several agencies, such as the National Science Foundation, the National Institute of Standard and Technology, and the Department of Energy. He has extensively published on various subjects, including time series classification, and process mining. His current research interests include the application of data mining, process mining, and optimization in design and analysis of manufacturing and healthcare systems

Georgiana Ifrim holds a Ph.D. and M.Sc. degree from Max-Planck Institute for Informatics, Germany, and a B.Sc. degree from University of Bucharest, Romania. She is an Assistant Professor at the School of Computer Science, University College Dublin, Ireland, Co-Lead of the SFI Centre for Research Training in Machine Learning (ML-Labs) and SFI Funded Investigator with the Insight Centre for Data Analytics and VistaMilk research centres. Dr. Ifrim's research focuses on developing scalable predictive models for machine learning and data mining applications. She has developed new methods for sequence learning, time series classification, text mining and real-time prediction for news and social streams. Her current research focuses on the design of efficient and interpretable learning models for sequences (e.g., DNA, time series), and on real-time prediction for streaming data (e.g., news and social media)

Patrick Schäfer holds a Ph.D. degree from Humboldt University of Berlin and a M.Sc. degree from Free University of Berlin, both in Computer Science. He is a Postdoc Researcher and Lecturer at the Humboldt University of Berlin. He Besides he worked at the Konrad Zuse Institute in Parallel and Distributed Systems in Berlin. His main research interests are scalable time series analytics and parallel and distributed systems. His current research is on early and scalable time series classification, time series motif discovery, and assessing the land use using satellite image time series

Diego Furtado Silva holds a M.Sc. and Ph.D. degrees in Computer Science and Computational Mathematics at the Institute of Mathematics and Computer Sciences (ICMC), University of São Paulo (USP), where he also graduated in Computer Science. He is an Assistant Professor at the Federal University of São Carlos. Besides, he has worked at the University of Columbia and the University of California, Riverside. His main research interests are time series mining, music information retrieval, and data stream classification. His current research approaches these domains using, primarily, meta-learning and deep learning techniques