Gözüyılmaz, ŞeymaKundakcıoğlu, Ömer Erhun2022-08-162022-08-162021-090171-6468http://hdl.handle.net/10679/7810https://doi.org/10.1007/s00291-021-00637-wDecomposing 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.engrestrictedAccessarticle43373375800065860530000110.1007/s00291-021-00637-wTime seriesSeasonal trend decompositionMixed integer nonlinear programming2-s2.0-85107560254