Identifying and reducing errors in remaining time prediction due to inter-case dynamics

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

    Abstract

    Remaining time prediction (RTP) is the problem of predicting the time until a specific process step is reached in a specific process instance. Feature engineering in established RTP techniques assume that cases progress in isolation. Intercase dynamics such as batching violate this assumption, leading to high prediction errors. Yet, existing RTP techniques do not consider the nature of prediction errors to improve quality. We contribute a technique for identifying the location and context of prediction errors by visually comparing prediction and ground truth. For the case of batching, we show how to engineer inter-case features that detail the impact of batching on the remaining time. Our evaluation shows that adding intercase features improves prediction performance across almost all evaluated primary prediction methods on two real-life event logs, with error reductions of up to 37%. We finally advocate for a more thorough and transparent evaluation of prediction errors in RTP research, including our own results.

    Original languageEnglish
    Title of host publicationProceedings - 2020 2nd International Conference on Process Mining, ICPM 2020
    EditorsBoudewijn van Dongen, Marco Montali, Moe Thandar Wynn
    PublisherInstitute of Electrical and Electronics Engineers
    Pages25-32
    Number of pages8
    ISBN (Electronic)9781728198323
    DOIs
    Publication statusPublished - Oct 2020
    Event2nd International Conference on Process Mining, ICPM 2020 - Virtual, Padua, Italy
    Duration: 4 Oct 20209 Oct 2020

    Conference

    Conference2nd International Conference on Process Mining, ICPM 2020
    CountryItaly
    CityVirtual, Padua
    Period4/10/209/10/20

    Keywords

    • batch processing
    • inter-case features
    • predictive process monitoring
    • remaining time prediction

    Fingerprint Dive into the research topics of 'Identifying and reducing errors in remaining time prediction due to inter-case dynamics'. Together they form a unique fingerprint.

    Cite this