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

Li Chen () and Yuanhua Feng ()
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Li Chen: Paderborn University
Yuanhua Feng: Paderborn University

No 177, Working Papers CIE from Paderborn University, CIE Center for International Economics

Abstract: 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.

Keywords: Time series forecasting; semiparametric algorithm; forecasting accuracy; deseats; SAP Analytics Cloud; enterprise adoption (search for similar items in EconPapers)
JEL-codes: C01 C02 (search for similar items in EconPapers)
Pages: 38 pages
Date: 2026-01
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