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Mathematical optimization for time series decomposition

Author

Listed:
  • Seyma Gozuyilmaz

    (Ozyegin University)

  • O. Erhun Kundakcioglu

    (Ozyegin University)

Abstract

Decomposing time series into trend and seasonality components reveals insights used in forecasting and anomaly detection. This study proposes a mathematical optimization approach that addresses several data-related issues in time series decomposition. Our approach does not only handle longer and multiple seasons but also identifies outliers and trend shifts. Numerical experiments on real-world and synthetic problem sets present the effectiveness of the proposed approach.

Suggested Citation

  • Seyma Gozuyilmaz & O. Erhun Kundakcioglu, 2021. "Mathematical optimization for time series decomposition," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 43(3), pages 733-758, September.
    • Handle: RePEc:spr:orspec:v:43:y:2021:i:3:d:10.1007_s00291-021-00637-w
    DOI: 10.1007/s00291-021-00637-w
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    References listed on IDEAS

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