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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 대한산업공학회 Industrial Engineering & Management Systems Industrial Engineering & Management Systems 제7권 제1호
- 발행연도
- 2008.6
- 수록면
- 9 - 22 (14page)
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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.
목차
Abstract
1. INTRODUCTION
2. LITERATURE REVIEW
3. BRIEF REVIEW OF THE S-E ALGORITHM
4. MAIN RESULTS
5. EXTENSION TO PERIODIC TIME SERIES MODELS
6. NUMERICAL EXAMPLES
7. DISCUSSION AND CONCLUSION
ACKNOWLEDGMENT
REFERENCES
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