23rd EANN / EAAAI 2022, 17 - 20 June 2022, Greece

Trend and Seasonality Elimination from Relational Data

Jan Motl, Pavel Kordík


  Detrending and deseasoning is a common preprocessing step in time-series analysis. We argue that the same preprocessing step should be considered on relational data whenever the observations are time-dependent. We applied Hierarchical Generalized Additive Models (HGAMs) to detrend and deseason (D&D) 18 real-world relational datasets. The observed positive effect of D&D on the predictive accuracy is statistically significant. The proposed method of D&D might be used to improve the predictive accuracy of churn, default, or propensity models, among others.  

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