IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v468y2017icp622-637.html
   My bibliography  Save this article

Distance measure with improved lower bound for multivariate time series

Author

Listed:
  • Li, Hailin

Abstract

Lower bound function is one of the important techniques used to fast search and index time series data. Multivariate time series has two aspects of high dimensionality including the time-based dimension and the variable-based dimension. Due to the influence of variable-based dimension, a novel method is proposed to deal with the lower bound distance computation for multivariate time series. The proposed method like the traditional ones also reduces the dimensionality of time series in its first step and thus does not directly apply the lower bound function on the multivariate time series. The dimensionality reduction is that multivariate time series is reduced to univariate time series denoted as center sequences according to the principle of piecewise aggregate approximation. In addition, an extended lower bound function is designed to obtain good tightness and fast measure the distance between any two center sequences. The experimental results demonstrate that the proposed lower bound function has better tightness and improves the performance of similarity search in multivariate time series datasets.

Suggested Citation

  • Li, Hailin, 2017. "Distance measure with improved lower bound for multivariate time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 468(C), pages 622-637.
  • Handle: RePEc:eee:phsmap:v:468:y:2017:i:c:p:622-637
    DOI: 10.1016/j.physa.2016.10.062
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S037843711630749X
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000

    File URL: https://libkey.io/10.1016/j.physa.2016.10.062?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. Li, Hailin, 2015. "Piecewise aggregate representations and lower-bound distance functions for multivariate time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 427(C), pages 10-25.
    2. Qiang Yang & Xindong Wu, 2006. "10 Challenging Problems In Data Mining Research," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 5(04), pages 597-604.
    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. DE CNUDDE, Sofie & MARTENS, David & EVGENIOU, Theodoros & PROVOST, Foster, 2017. "A benchmarking study of classification techniques for behavioral data," Working Papers 2017005, University of Antwerp, Faculty of Business and Economics.
    2. Harshita Patel & Dharmendra Singh Rajput & G Thippa Reddy & Celestine Iwendi & Ali Kashif Bashir & Ohyun Jo, 2020. "A review on classification of imbalanced data for wireless sensor networks," International Journal of Distributed Sensor Networks, , vol. 16(4), pages 15501477209, April.
    3. Qi Liu & Gengzhong Feng & Nengmin Wang & Giri Kumar Tayi, 2018. "A multi-objective model for discovering high-quality knowledge based on data quality and prior knowledge," Information Systems Frontiers, Springer, vol. 20(2), pages 401-416, April.
    4. Liao, Jui-Jung & Shih, Ching-Hui & Chen, Tai-Feng & Hsu, Ming-Fu, 2014. "An ensemble-based model for two-class imbalanced financial problem," Economic Modelling, Elsevier, vol. 37(C), pages 175-183.
    5. Vilém Novák & Soheyla Mirshahi, 2021. "On the Similarity and Dependence of Time Series," Mathematics, MDPI, vol. 9(5), pages 1-14, March.
    6. Riesgo García, María Victoria & Krzemień, Alicja & Manzanedo del Campo, Miguel Ángel & Escanciano García-Miranda, Carmen & Sánchez Lasheras, Fernando, 2018. "Rare earth elements price forecasting by means of transgenic time series developed with ARIMA models," Resources Policy, Elsevier, vol. 59(C), pages 95-102.
    7. Pancheng Wang & Shasha Li & Haifang Zhou & Jintao Tang & Ting Wang, 2019. "Cited text spans identification with an improved balanced ensemble model," Scientometrics, Springer;Akadémiai Kiadó, vol. 120(3), pages 1111-1145, September.
    8. Ionuţ ŢĂRANU, 2016. "Data mining in healthcare: decision making and precision," Database Systems Journal, Academy of Economic Studies - Bucharest, Romania, vol. 6(4), pages 33-40, May.
    9. Keng-Hoong Ng & Chin-Kuan Ho & Somnuk Phon-Amnuaisuk, 2012. "A Hybrid Distance Measure for Clustering Expressed Sequence Tags Originating from the Same Gene Family," PLOS ONE, Public Library of Science, vol. 7(10), pages 1-14, October.
    10. Qi Liu & Gengzhong Feng & Nengmin Wang & Giri Kumar Tayi, 0. "A multi-objective model for discovering high-quality knowledge based on data quality and prior knowledge," Information Systems Frontiers, Springer, vol. 0, pages 1-16.
    11. Hady Suryono & Heri Kuswanto & Nur Iriawan, 2022. "Two-Phase Stratified Random Forest for Paddy Growth Phase Classification: A Case of Imbalanced Data," Sustainability, MDPI, vol. 14(22), pages 1-13, November.
    12. Yan Li & Manoj Thomas & Kweku-Muata Osei-Bryson & Jason Levy, 2016. "Problem Formulation in Knowledge Discovery via Data Analytics (KDDA) for Environmental Risk Management," IJERPH, MDPI, vol. 13(12), pages 1-17, December.
    13. Neda Abdelhamid & Arun Padmavathy & David Peebles & Fadi Thabtah & Daymond Goulder-Horobin, 2020. "Data Imbalance in Autism Pre-Diagnosis Classification Systems: An Experimental Study," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 19(01), pages 1-16, March.

    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:eee:phsmap:v:468:y:2017:i:c:p:622-637. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    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.