Predictive Econometrics and Big Data

  • Vladik Kreinovich
  • Songsak Sriboonchitta
  • Nopasit Chakpitak
Conference proceedings TES 2018

Part of the Studies in Computational Intelligence book series (SCI, volume 753)

Table of contents

  1. Front Matter
    Pages i-xii
  2. Keynote Address

    1. Front Matter
      Pages 1-1
    2. Chaitanya Baru
      Pages 3-17
  3. Fundamental Theory

    1. Front Matter
      Pages 19-19
    2. F. Jay Breidt, Jean D. Opsomer, Chien-Min Huang
      Pages 21-35
    3. Marc S. Paolella, Paweł Polak
      Pages 36-77
    4. Cathy W. S. Chen, Yu-Wen Sun
      Pages 122-145
    5. Thongchai Dumrongpokaphan, Vladik Kreinovich
      Pages 177-181
    6. Dung Tien Nguyen, Son P. Nguyen, Uyen H. Pham, Thien Dinh Nguyen
      Pages 182-191
    7. Vladik Kreinovich, Thongchai Dumrongpokaphan, Hung T. Nguyen, Olga Kosheleva
      Pages 198-204
    8. Vladik Kreinovich, Hung T. Nguyen, Songsak Sriboonchitta, Olga Kosheleva
      Pages 205-213
    9. Vladik Kreinovich, Songsak Sriboonchitta
      Pages 214-221
    10. Ziwei Ma, Weizhong Tian, Baokun Li, Tonghui Wang
      Pages 222-232
    11. Ziwei Ma, Xiaonan Zhu, Tonghui Wang, Kittawit Autchariyapanitkul
      Pages 233-245

About these proceedings


This book presents recent research on predictive econometrics and big data. Gathering edited papers presented at the 11th International Conference of the Thailand Econometric Society (TES2018), held in Chiang Mai, Thailand, on January 10-12, 2018, its main focus is on predictive techniques – which directly aim at predicting economic phenomena; and big data techniques – which enable us to handle the enormous amounts of data generated by modern computers in a reasonable time. The book also discusses the applications of more traditional statistical techniques to econometric problems.

Econometrics is a branch of economics that employs mathematical (especially statistical) methods to analyze economic systems, to forecast economic and financial dynamics, and to develop strategies for achieving desirable economic performance. It is therefore important to develop data processing techniques that explicitly focus on prediction. The more data we have, the better our predictions will be. As such, these techniques are essential to our ability to process huge amounts of available data.


Computational Intelligence Econometrics Precitive Econometrics Big Data Models of Economic Phenomena TES 2018

Editors and affiliations

  • Vladik Kreinovich
    • 1
  • Songsak Sriboonchitta
    • 2
  • Nopasit Chakpitak
    • 3
  1. 1.Computer Science DepartmentUniversity of Texas at El PasoEl PasoUSA
  2. 2.International CollegeChiang Mai UniversityChiang MaiThailand
  3. 3.International CollegeChiang Mai UniversityChiang MaiThailand

Bibliographic information

  • DOI
  • Copyright Information Springer International Publishing AG 2018
  • Publisher Name Springer, Cham
  • eBook Packages Engineering
  • Print ISBN 978-3-319-70941-3
  • Online ISBN 978-3-319-70942-0
  • Series Print ISSN 1860-949X
  • Series Online ISSN 1860-9503
  • About this book