• Generalife Palace
  • Alhambra View
  • Alhambra's Night
  • Granada's Panoramic (I)
  • Granada's Panoramic (II)
  • Granada's Cathedral
  • Moorish Windows
  • Court of the Lions
  • Costa Tropical of Granada
Generalife Palace1 Alhambra View2 Alhambra's Night3 Granada's Panoramic (I)4 Granada's Panoramic (II)5 Granada's Cathedral6 Moorish Windows7 Court of the Lions8 Costa Tropical of Granada9
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Special Session Proposals

SS1 Dimensionality reduction and Similarity measures for Time series data analysis and its applications.

Due to availability of cheap sensors, storing devices and wireless communication, time series data for various applications are now available. Their analysis is needed in various fields from medical and health related applications, environment monitoring, biometrics, process industry, finance and economic data analysis or weather prediction. Analysis of time series data includes classification, recognition or prediction. In any case, for processing of large time series data, efficient representation of the data as well as proper dimensionality reduction is important. Similarity measures play an important role in classification or clustering of time series data, which is vital in many analysis.

The objective of this special session is to bring together researchers, and practitioners working on theoretical aspects or various applications of time-series data, to provide them an interdisciplinary and multidisciplinary forum for discussion of different approaches and techniques for efficient time series data processing and their applications in various practical fields. We will specially emphasize on proposals of efficient representation, techniques for dimensionality reduction and similarity measures.

The topics of interest include but are not limited to:

  • Representation of time series data
  • Feature selection/ dimensionality reduction for time series data
  • Similarity measures for comparison of time series
  • Classification and clustering of multivariate time series data
  • Time series text data analysis
  • Anomaly detection from time series data or signal
  • Applications of time series analysis in various practical fields like energy, finance, medical, health, environment, network, social data, transportation, weather etc.
Prof. Basabi Chakraborty, Pattern Recognition and Machine Learning Lab Faculty of Software and Information Science, Iwate Prefectural University, Japan
Prof. Goutam Chakraborty, Intelligent Informatics Lab Faculty of Software and Information Science, Iwate Prefectural University, Japan
SS2 Energy time series forecasting: new approaches and applications.

This Special Session focuses on the forecasting of time series, with particular emphasis on energy related data. By energy, we understand any kind of energy, such as electrical, solar, microwave, wind, etc.

Time series and forecasting methods continue to improve due to the enhancements in computing power that allows for a closer examination of economic phenomenon. In this Special Session, we invite authors to submit their original research on exploring the issues and applications of energy time series and forecasting.

The topics of interest include but are not limited to:

  • Energy-related time series analysis.
  • Energy-related time series models.
  • Energy-related time series forecasting.
  • Non-parametric time series approaches.
Dr. Francisco Martínez-Álvarez , Department of Computer Science, Pablo de Olavide University of Seville, Spain
Dr. Alicia Troncoso , Department of Computer Science, Pablo de Olavide University, Spain.
Dr. Neeraj D. Bokde. , Department of Electronics and Communication Engineering, Visvesvaraya National Institute of Technology, India.
SS3 Recent Developments on Time-Series Modelling for Financial Data.

This session aims at presenting the recent developments of Time Series Modelling applied to financial and energy futures data. In particular, a focus is on studies that develop and apply recent nonlinear econometric models to reproduce financial market dynamics, capture financial data properties (asymmetry, volatility clustering, kurtosis excess, nonmorality, etc.). Papers on high frequency data and nonparametric econometric models are particularly welcomed.

Applications on developed and emerging stock markets, exchange rate markets, energy future and commodity markets, real estate markets, which offer an international comparison are considered in priority. Besides the empirical application, the papers should include a good economic analysis and develop the policy implications of the empirical findings.

Prof. Dr. Fredj Jawadi , University of Evry and EconomiX, France
SS4 Computational Intelligence methods for Time Series

Within the field of science and engineering, it is very common to have data arranged in the form of time series data which must be subsequently analyzed, modeled and classified with the eventual goal of predicting future values. The literature shows that all these tasks related to time series can be undertaken using computational intelligence methods. In fact, new and further computational intelligence approaches, their efficiency and their comparison to statistical methods and other fact-checked computational intelligence methods, is a significant topic in academic and professional projects and works.

Therefore, this special session aims at showing to our research community high quality and state of the art computational intelligence (and statistical) related works, applied to time series data and their tasks: analysis, forecasting, classification, and clustering. Furthermore, the experts can, from the starting point that the works shown provide, discuss different solutions and research issues for these topics.

The topics of interest include but are not limited to:

Computational Intelligence (CI) techniques applied to

  • time series analysis
  • time series modeling
  • time series forecasting
  • time series classification
  • time series clustering
  • statistical and CI techniques for time series, comparative evaluation and/or novel propositions
  • Organizer:
    Prof. Dr. Héctor Pomares , Dep. Computer Architecture and Computer Technology, University of Granada, Spain
    Prof. Dr. German Gutierrez , Dep. Computer Science, E.P.S. University Carlos III of Madrid, Spain
SS5 Future of Mathematical and Logical Structures behind Time Series Analysis and History

This session is to present mathematical and logical structures, methodology and theory that could be used with time-series and also that has been used so far. It also aims to bring into existence recent and becoming developments in computational mathematics that could be used in the field of time series. It will present logical, mathematical and historical backgrounds behind time series analysis. Papers that concentrate on future developments are especially welcome.

Suggestions for new development areas for time series, computational logic applied to time series, historical comparison with old and new methods etc.

The topics of interest include but are not limited to:

  • Computational logic applied to time series analysis e.g. similarities etc.
  • New methods in neural networks applied to time series e.g. LSTM etc.
  • Mathematical structures behind time series
  • Data mining and time series
  • Historical reviews of time series e.g. comparisons of new and old methods
Prof. Dr. Kalle Saastamoinen , Department of Military Technology, National Defence University,Helsinki, Finland
SS6 Structural Time Series Models

SS7 Recent Developments on Time-Series Modelling.