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
    12. Akira Namatame
      Pages 255-265
    13. Weizhong Tian, Tonghui Wang, Fengrong Wei, Fang Dai
      Pages 273-286
    14. Xiaonan Zhu, Baokun Li, Mixia Wu, Tonghui Wang
      Pages 287-302
    15. Xiaonan Zhu, Tonghui Wang, S. T. Boris Choy, Kittawit Autchariyapanitkul
      Pages 303-317
  4. Applications

    1. Front Matter
      Pages 319-319
    2. Tzong-Ru (Jiun-Shen) Lee, Yu-Ting Huang, Man-Yu Huang, Huan-Yu Chen
      Pages 321-338
    3. Petchaluck Boonyakunakorn, Pathairat Pastpipatkul, Songsak Sriboonchitta
      Pages 339-349
    4. Noppasit Chakpitak, Paravee Maneejuk, Somsak Chanaim, Songsak Sriboonchitta
      Pages 350-362
    5. Saowaluk Duangin, Jirakom Sirisrisakulchai, Songsak Sriboonchitta
      Pages 375-391
    6. Massoud Moslehpour, Ha Le Thi Thanh, Pham Van Kien
      Pages 392-407
    7. Chalerm Jaitang, Paravee Maneejuk, Aree Wiboonpongse, Songsak Sriboonchitta
      Pages 408-421
    8. Rossarin Osathanunkul, Chatchai Khiewngamdee, Woraphon Yamaka, Songsak Sriboonchitta
      Pages 422-429
    9. Munkh-Ulzii, Massoud Moslehpour, Pham Van Kien
      Pages 464-491
    10. Kobpongkit Navapan, Petchaluck Boonyakunakorn, Songsak Sriboonchitta
      Pages 492-501
    11. Rossarin Osathanunkul, Natthaphat Kingnetr, Songsak Sriboonchitta
      Pages 517-535
    12. Rungrapee Phadkantha, Woraphon Yamaka, Roengchai Tansuchat
      Pages 536-548
    13. Pichayakone Rakpho, Woraphon Yamaka, Roengchai Tansuchat
      Pages 549-562
    14. Meng-Chun Susan Shen, I-Tien Chu, Wan-Tran Huang
      Pages 563-572
    15. Songsak Sriboonchitta, Chukiat Chaiboonsri, Jittima Singvejsakul
      Pages 573-589
    16. Teerawut Teetranont, Woraphon Yamaka, Songsak Sriboonchitta
      Pages 600-612
    17. Teerawut Teetranont, Woraphon Yamaka, Songsak Sriboonchitta
      Pages 613-628
    18. Duangthip Sirikanchanarak, Tanaporn Tungtrakul, Songsak Sriboonchitta
      Pages 629-642
    19. Jirawan Suwannajak, Woraphon Yamaka, Songsak Sriboonchitta, Roengchai Tansuchat
      Pages 643-655

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 Engineering (R0)
  • Print ISBN 978-3-319-70941-3
  • Online ISBN 978-3-319-70942-0
  • Series Print ISSN 1860-949X
  • Series Online ISSN 1860-9503
  • Buy this book on publisher's site