IDEAS home Printed from https://ideas.repec.org/a/spr/jglopt/v86y2023i4d10.1007_s10898-023-01294-9.html
   My bibliography  Save this article

An average-compress algorithm for the sample mean problem under dynamic time warping

Author

Listed:
  • Brijnesh Jain

    (OTH Regensburg)

  • Vincent Froese

    (TU Berlin)

  • David Schultz

    (TU Berlin)

Abstract

Computing a sample mean of time series under dynamic time warping is NP-hard. Consequently, there is an ongoing research effort to devise efficient heuristics. The majority of heuristics have been developed for the constrained sample mean problem that assumes a solution of predefined length. In contrast, research on the unconstrained sample mean problem is underdeveloped. In this article, we propose a generic average-compress (AC) algorithm to address the unconstrained problem. The algorithm alternates between averaging (A-step) and compression (C-step). The A-step takes an initial guess as input and returns an approximation of a sample mean. Then the C-step reduces the length of the approximate solution. The compressed approximation serves as initial guess of the A-step in the next iteration. The purpose of the C-step is to direct the algorithm to more promising solutions of shorter length. The proposed algorithm is generic in the sense that any averaging and any compression method can be used. Experimental results show that the AC algorithm substantially outperforms current state-of-the-art algorithms for time series averaging.

Suggested Citation

  • Brijnesh Jain & Vincent Froese & David Schultz, 2023. "An average-compress algorithm for the sample mean problem under dynamic time warping," Journal of Global Optimization, Springer, vol. 86(4), pages 885-903, August.
    • Handle: RePEc:spr:jglopt:v:86:y:2023:i:4:d:10.1007_s10898-023-01294-9
    DOI: 10.1007/s10898-023-01294-9
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10898-023-01294-9
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10898-023-01294-9?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.

    References listed on IDEAS

    as
    1. Marco Cuturi & Mathieu Blondel, 2017. "Soft-DTW: a Differentiable Loss Function for Time-Series," Working Papers 2017-81, Center for Research in Economics and Statistics.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Dimitrios Kontogiannis & Dimitrios Bargiotas & Aspassia Daskalopulu & Lefteri H. Tsoukalas, 2021. "A Meta-Modeling Power Consumption Forecasting Approach Combining Client Similarity and Causality," Energies, MDPI, vol. 14(19), pages 1-19, September.
    2. Hao Luo & Kexin Sun & Junlu Wang & Chengfeng Liu & Linlin Ding & Baoyan Song, 2019. "Multistage identification method for real-time abnormal events of streaming data," International Journal of Distributed Sensor Networks, , vol. 15(12), pages 15501477198, December.
    3. Planakis, Nikolaos & Papalambrou, George & Kyrtatos, Nikolaos, 2022. "Ship energy management system development and experimental evaluation utilizing marine loading cycles based on machine learning techniques," Applied Energy, Elsevier, vol. 307(C).
    4. Mario Flor & Sergio Herraiz & Ivan Contreras, 2021. "Definition of Residential Power Load Profiles Clusters Using Machine Learning and Spatial Analysis," Energies, MDPI, vol. 14(20), pages 1-15, October.
    5. Westermann, Paul & Deb, Chirag & Schlueter, Arno & Evins, Ralph, 2020. "Unsupervised learning of energy signatures to identify the heating system and building type using smart meter data," Applied Energy, Elsevier, vol. 264(C).

    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:spr:jglopt:v:86:y:2023:i:4:d:10.1007_s10898-023-01294-9. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.