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Mining Association of Outliers in Time Series

In: Recent Advancements in Tourism Business, Technology and Social Sciences

Author

Listed:
  • Maria Katsouda

    (University of Patras)

  • Konstantinos Kollias

    (Democritus University of Thrace)

  • Constantinos Halkiopoulos

    (University of Patras)

  • Basilis Boutsinas

    (University of Patras)

Abstract

Outliers or extreme values are patterns in the data, which do not conform to a well-defined concept of normal behavior. In today's often changing environment, detecting and forecasting outliers in time series related to stock market, credit card fraud, fraud in insurance systems, tourism demand indicators, etc., is a challenge for both humans and computers. In this paper, we present, for the first time, the association among the outliers in different univariate time series, and we formally define Mining Association of Extreme Values (MAEV). We then investigate how MAEV can be applied to forecasting outliers in a time series based on the detection of outliers in another. We evaluate the efficiency of the proposed methodology by applying it to hotel booking demand. More specifically, we first use an algorithm for automatically detecting outliers in time series such as booking volumes, arrival volumes, or booking cancellations, then we form a set of instances that correspond to time intervals, by considering in each instance the existence or not of an outliers for every different time series, then we apply Apriori association rule mining algorithm to the formed set of instances, and finally, we use the extracted association rules to forecast more outliers.

Suggested Citation

  • Maria Katsouda & Konstantinos Kollias & Constantinos Halkiopoulos & Basilis Boutsinas, 2024. "Mining Association of Outliers in Time Series," Springer Proceedings in Business and Economics, in: Vicky Katsoni & George Cassar (ed.), Recent Advancements in Tourism Business, Technology and Social Sciences, pages 433-444, Springer.
    • Handle: RePEc:spr:prbchp:978-3-031-54342-5_26
    DOI: 10.1007/978-3-031-54342-5_26
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    More about this item

    Keywords

    Outlier detection; Time series analysis; Association rule mining; Tourism demand analysis; Hotel booking demand forecasting;
    All these keywords.

    JEL classification:

    • O32 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Management of Technological Innovation and R&D
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • Z32 - Other Special Topics - - Tourism Economics - - - Tourism and Development
    • L86 - Industrial Organization - - Industry Studies: Services - - - Information and Internet Services; Computer Software

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