Time series forecasting in SAP using a data-driven seasonal semiparametric ARMA model

Building upon our previous work that integrated a semi-parametric ARMA model into the SAP ecosystem, this paper introduces an enhanced forecasting application for SAP Analytics Cloud (SAC), termed deseatsForecast. The application leverages a data-driven seasonal semiparametric ARMA (S-Semi-ARMA) algorithm and novelly addresses two critical gaps in the practical deployment of advanced semiparametric models within enterprise environments. Specifically, the proposed deseatsForecast application enables robust estimation of slowly-changing seasonal patterns jointly with trend components through a data-driven Iterative Plug-In (IPI) algorithm for bandwidth selection. Secondly, the application provides native support for panel data structures, thereby extending its applicability to multidimensional business datasets commonly encountered in enterprise settings. The paper begins with a review of the data-driven S-Semi-ARMA model and the estimation procedures for trend, seasonal, and residual components. Subsequently, forecasting techniques based on the S-Semi-ARMA framework are presented, followed by a brief description of the architecture and design of the deseatsForecast application, with particular emphasis on its extensions relative to the smootsForecast application. Finally, the forecasting application is empirically validated using OECD passenger car registration data for multiple countries and a comparative study against SAP’s autoML-based forecasting approach is conducted. The empirical results demonstrate consistently strong forecast performance of the deseatsForecast application and highlight its superior forecast accuracy and transparency compared with the current autoML approach in SAP.