IDEAS home Printed from https://ideas.repec.org/h/spr/sprchp/978-981-96-2004-3_18.html
   My bibliography  Save this book chapter

Window Size Effects in Time Series Anomaly Detection: An Evaluation of Pitchy Anomaly Detection

In: Directional and Multivariate Statistics

Author

Listed:
  • Ekin Can Erkuş

    (Turkey R&D Center, Intelligent Application DC, Huawei Technologies Co. Ltd)

  • Vilda Purutçuoğlu

    (Middle East Technical University, Department of Statistics)

Abstract

Real-time anomaly detection in time series data is crucial for various applications and requires several parameters to be defined prior to the proper analyses. The window size is one of those parameters which indicates the batch data size that is analyzed in each iteration. A recently published Pitchy Anomaly Detection (PAD) algorithm, which combines spectral and time-domain analysis, is used to investigate the effects of window size on anomaly detection performance and computational complexities. The study uses two pre-annotated ECG datasets, and a synthetically generated dataset under different data size conditions to perform the experiments. Results show that smaller window sizes generally lead to improved accuracy and F1-scores, especially in detecting ventricular anomalies. However, excessively small windows can lead to a loss of contextual information and negatively impact overall performance.

Suggested Citation

  • Ekin Can Erkuş & Vilda Purutçuoğlu, 2025. "Window Size Effects in Time Series Anomaly Detection: An Evaluation of Pitchy Anomaly Detection," Springer Books, in: Somesh Kumar & Barry C. Arnold & Kunio Shimizu & Arnab Kumar Laha (ed.), Directional and Multivariate Statistics, pages 349-361, Springer.
    • Handle: RePEc:spr:sprchp:978-981-96-2004-3_18
    DOI: 10.1007/978-981-96-2004-3_18
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a
    for a similarly titled item that would be available.

    More about this item

    Statistics

    Access and download statistics

    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:sprchp:978-981-96-2004-3_18. 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: 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.