IDEAS home Printed from https://ideas.repec.org/a/wsi/ijitdm/v12y2013i03ns0219622013500193.html
   My bibliography  Save this article

An Integrated Data Characteristic Testing Scheme For Complex Time Series Data Exploration

Author

Listed:
  • LING TANG

    (School of Economics and Management, Beijing University of Chemical Technology, Beijing 100029, China;
    Centre for Energy Chemical Management, Beijing University of Chemical Technology, Beijing 100029, China)

  • LEAN YU

    (School of Economics and Management, Beijing University of Chemical Technology, Beijing 100029, China;
    Centre for Energy Chemical Management, Beijing University of Chemical Technology, Beijing 100029, China;
    Alibaba Business College, Hangzhou Normal University, Hangzhou 310036, China;
    College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China)

  • FANGTAO LIU

    (Department of Management Science, City University of Hong Kong, Hong Kong, China)

  • WEIXUAN XU

    (Institute of Policy and Management, Chinese Academy of Sciences, Beijing 100190, China)

Abstract

In this paper, an integrated data characteristic testing scheme is proposed for complex time series data exploration so as to select the most appropriate research methodology for complex time series modeling. Based on relationships across different data characteristics, data characteristics of time series data are divided into two main categories: nature characteristics and pattern characteristics in this paper. Accordingly, two relevant tasks, nature determination and pattern measurement, are involved in the proposed testing scheme. In nature determination, dynamics system generating the time series data is analyzed via nonstationarity, nonlinearity and complexity tests. In pattern measurement, the characteristics of cyclicity (and seasonality), mutability (or saltation) and randomicity (or noise pattern) are measured in terms of pattern importance. For illustration purpose, four main Chinese economic time series data are used as testing targets, and the data characteristics hidden in these time series data are thoroughly explored by using the proposed integrated testing scheme. Empirical results reveal that the natures of all sample data demonstrate complexity in the phase of nature determination, and in the meantime the main pattern of each time series is captured based on the pattern importance, indicating that the proposed scheme can be used as an effective data characteristic testing tool for complex time series data exploration from a comprehensive perspective.

Suggested Citation

  • Ling Tang & Lean Yu & Fangtao Liu & Weixuan Xu, 2013. "An Integrated Data Characteristic Testing Scheme For Complex Time Series Data Exploration," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 12(03), pages 491-521.
  • Handle: RePEc:wsi:ijitdm:v:12:y:2013:i:03:n:s0219622013500193
    DOI: 10.1142/S0219622013500193
    as

    Download full text from publisher

    File URL: http://www.worldscientific.com/doi/abs/10.1142/S0219622013500193
    Download Restriction: Access to full text is restricted to subscribers

    File URL: https://libkey.io/10.1142/S0219622013500193?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yixiang Ma & Lean Yu & Guoxing Zhang, 2022. "A Hybrid Short-Term Load Forecasting Model Based on a Multi-Trait-Driven Methodology and Secondary Decomposition," Energies, MDPI, vol. 15(16), pages 1-20, August.
    2. Lean Yu & Yueming Ma, 2021. "A Data-Trait-Driven Rolling Decomposition-Ensemble Model for Gasoline Consumption Forecasting," Energies, MDPI, vol. 14(15), pages 1-26, July.
    3. Yu, Lean & Wang, Zishu & Tang, Ling, 2015. "A decomposition–ensemble model with data-characteristic-driven reconstruction for crude oil price forecasting," Applied Energy, Elsevier, vol. 156(C), pages 251-267.
    4. Tang, Ling & Yu, Lean & He, Kaijian, 2014. "A novel data-characteristic-driven modeling methodology for nuclear energy consumption forecasting," Applied Energy, Elsevier, vol. 128(C), pages 1-14.
    5. Lean Yu & Lihang Yu & Kaitao Yu, 2021. "A high-dimensionality-trait-driven learning paradigm for high dimensional credit classification," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-20, December.
    6. Lean Yu & Zebin Yang & Ling Tang, 2016. "Prediction-Based Multi-Objective Optimization for Oil Purchasing and Distribution with the NSGA-II Algorithm," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 15(02), pages 423-451, March.
    7. Ling Tang & Shuai Wang & Kaijian He & Shouyang Wang, 2015. "A novel mode-characteristic-based decomposition ensemble model for nuclear energy consumption forecasting," Annals of Operations Research, Springer, vol. 234(1), pages 111-132, November.
    8. Yu, Lean & Zhao, Yang & Tang, Ling, 2014. "A compressed sensing based AI learning paradigm for crude oil price forecasting," Energy Economics, Elsevier, vol. 46(C), pages 236-245.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wsi:ijitdm:v:12:y:2013:i:03:n:s0219622013500193. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Tai Tone Lim (email available below). General contact details of provider: http://www.worldscinet.com/ijitdm/ijitdm.shtml .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.