Mathematical optimization for time series decomposition

Seyma Gozuyilmaz and O. Erhun Kundakcioglu ()
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Seyma Gozuyilmaz: Ozyegin University
O. Erhun Kundakcioglu: Ozyegin University

OR Spectrum: Quantitative Approaches in Management, 2021, vol. 43, issue 3, No 6, 733-758

Abstract: 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.

Keywords: Time series; Seasonal trend decomposition; Mixed integer nonlinear programming (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s00291-021-00637-w

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