Forecasting of trend stationary time series in SAP using a data-driven semiparametric ARMA model

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

No 179, Working Papers Dissertations from Paderborn University, Faculty of Business Administration and Economics

Abstract: Motivated by more and more semi- or nonparametric models applied in time series forecasting and their demonstrated superior performance in many empirical researches, this paper explores the adoption and integration of a semiparametric ARMA model in an enterprise system landscape. We begin by reviewing basic construction of the semiparametric ARMA model, the iterative plug-in algorithm for estimating the trend component of trend stationary times series, forecast techniques and quality measurements, which were well researched and published with the R package smoots. Subsequently, we showcase a novel approach to adopt the semiparametric ARMA model in a forecast application based on SAP Analytics Cloud (SAC), which leverages the platform’s strengths in system integrity, state-of-the-art user interface (UI) design as well as seamless connection to a R engine with smoots package embedded. The forecast application addresses key challenges in terms of cost efficiency, user experience, and the requirement for in-house statistical or machine learning expertise while adopting such statistical algorithms in enterprise context. Finally, we empirically evaluate the forecast quality of the integrated semiparametric ARMA model using real-world data, demonstrating promising results overall.

Keywords: Time series forecasting; semiparametric algorithm; forecasting accuracy; smoots package; SAP Analytics Cloud; enterprise adoption (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Pages: 39
Date: 2025-08
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