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Time Series Analysis and Forecasting

Selected Contributions from ITISE 2017

  • Ignacio Rojas
  • Héctor Pomares
  • Olga Valenzuela
Conference proceedings ITISE 2017

Part of the Contributions to Statistics book series (CONTRIB.STAT.)

Table of contents

  1. Front Matter
    Pages i-xiii
  2. Advanced Mathematical Methodologies in Time Series

  3. Computational Intelligence Methods for Time Series

    1. Front Matter
      Pages 93-93
    2. Marijana Cosovic, Slobodan Obradovic, Emina Junuz
      Pages 95-113
  4. Dimensionality Reduction and Similarity Measures in Time Series

    1. Front Matter
      Pages 129-129
    2. Matúš Maciak
      Pages 131-145
    3. Goutam Chakraborty, Takuya Kamiyama, Hideyuki Takahashi, Tetsuo Kinoshita
      Pages 147-157
    4. Marc Haßler, Christian Kohlschein, Tobias Meisen
      Pages 159-171
  5. Econometric Models

    1. Front Matter
      Pages 185-185
    2. Laila Ait Hassou, Fadoua Badaoui, Okou Guei Cyrille, Amine Amar, Abdelhak Zoglat, Elhadj Ezzahid
      Pages 217-228
  6. Energy Time Series Forecasting

    1. Front Matter
      Pages 229-229
    2. JingJie Chen, YongPing Zhang
      Pages 231-243
    3. David Rodriguez-Lozano, Juan A. Gomez-Pulido, Arturo Duran-Dominguez
      Pages 245-257
  7. Forecasting in Real Problems

    1. Front Matter
      Pages 273-273
    2. Teimuraz Matcharashvili, Natalia Zhukova, Tamaz Chelidze, Evgeni Baratashvili, Tamar Matcharashvili, Manana Janiashvili
      Pages 275-287
    3. Márton Ispány, Valdério A. Reisen, Glaura C. Franco, Pascal Bondon, Higor H. A. Cotta, Paulo R. P. Filho et al.
      Pages 289-308
    4. Mariko Kimura, Hyungsuk Tak, Taichi Kato
      Pages 309-321
  8. Back Matter
    Pages 339-340

About these proceedings

Introduction

This book presents selected peer-reviewed contributions from the International Work-Conference on Time Series, ITISE 2017, held in Granada, Spain, September 18-20, 2017. It discusses topics in time series analysis and forecasting, including advanced mathematical methodology, computational intelligence methods for time series, dimensionality reduction and similarity measures, econometric models, energy time series forecasting, forecasting in real problems, online learning in time series as well as high-dimensional and complex/big data time series.

The series of ITISE conferences provides a forum for scientists, engineers, educators and students to discuss the latest ideas and implementations in the foundations, theory, models and applications in the field of time series analysis and forecasting. It focuses on interdisciplinary and multidisciplinary research encompassing computer science, mathematics, statistics and econometrics.

Keywords

62-XX, 68-XX, 60-XX, 58-XX, 37-XX Time series Forecasting Mathematical methodology for time series Computational intelligence methods Forecasting in real problems Energy time series forecasting Dimensionality reduction Similarity measures Econometric models High-dimensional data Complex data Big data Artificial intelligence Pattern recognition On-line learning in time series

Editors and affiliations

  • Ignacio Rojas
    • 1
  • Héctor Pomares
    • 2
  • Olga Valenzuela
    • 3
  1. 1.ETSIITUniversity of GranadaGranadaSpain
  2. 2.CITIC-UGR and ETSIITUniversity of GranadaGranadaSpain
  3. 3.Faculty of SciencesUniversity of GranadaGranadaSpain

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-96944-2
  • Copyright Information Springer Nature Switzerland AG 2018
  • Publisher Name Springer, Cham
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-3-319-96943-5
  • Online ISBN 978-3-319-96944-2
  • Series Print ISSN 1431-1968
  • Buy this book on publisher's site