 Analysis of the performance of competing models for aggregate demand forecasting using observable data characteristicsN. Jayasree, T. Radha Ramanan and R. Sridharan International Journal of Industrial and Systems Engineering, 2020, vol. 35, issue 4, 410-432 Abstract: This paper provides an analysis of the performance of two simplified approaches for forecasting the aggregate demand for a product family consisting of two interrelated items. The correlated demand characteristic of the product family is modelled by bivariate vector IMA(1, 1) process. The forecasting approaches chosen for evaluation are univariate scheme and aggregate scheme. The performances of these methods are analysed by theoretically determining the forecast mean square error (MSE) in terms of observable data characteristics. Further, subsequent evaluation of percentage changes of these MSEs with respect to the MSE of original bivariate model has been conducted to determine how far the forecast MSE values deviate from the original model. Through numerical experimentation, the necessary and sufficient conditions in terms of observable data characteristics are determined for the two approaches to be equal in terms of forecast MSE of the original bivariate model. Keywords: aggregate demand forecast; vector IMA(1, 1) model; observable data characteristics. (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:ids:ijisen:v:35:y:2020:i:4:p:410-432 Access Statistics for this article More articles in International Journal of Industrial and Systems Engineering from Inderscience Enterprises Ltd |
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