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A New Algorithm for Automated Modeling of Seasonal Time Series Using Box-Jenkins Techniques  

Song, Qiang (R&D, RedPrairie Corporation)
Esogbue, Augustine O. (H. Milton Stewart School of Industrial and Systems Engineering Georgia Institute of Technology)
Publication Information
Industrial Engineering and Management Systems / v.7, no.1, 2008 , pp. 9-22 More about this Journal
Abstract
As an extension of a previous work by the authors (Song and Esogbue, 2006), a new algorithm for automated modeling of nonstationary seasonal time series is presented in this paper. Issues relative to the methodology for building automatically seasonal time series models and periodic time series models are addressed. This is achieved by inspecting the trend, estimating the seasonality, determining the orders of the model, and estimating the parameters. As in our previous work, the major instruments used in the model identification process are correlograms of the modeling errors while the least square method is used for parameter estimation. We provide numerical illustrations of the performance of the new algorithms with respect to building both seasonal time series and periodic time series models. Additionally, we consider forecasting and exercise the models on some sample time series problems found in the literature as well as real life problems drawn from the retail industry. In each instance, the models are built automatically avoiding the necessity of any human intervention.
Keywords
Automated Model Building Algorithms; Box-Jenkins Modeling Technique; Correlograms; Forecasting of Seasonal Time Series; Periodic Time Series Models; Seasonal Time Series; Seasonal Time Series Models;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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