On Periodic Asymmetric Extrapolation
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In this paper, we develop a new technique for the asymmetric approximation of discrete functions arising in seasonal customer demand extrapolation. We adapt the technique for two different settings, the so-called pull and push models. Our main goal here is to find effectively extrapolations minimizing the loss. For bothmodels, we discuss several features related to sampling, approximation, and extrapolation.
Keywordsperiodic functions approximation pull and push models maximizers
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- 2.R. G. Brown, Smoothing, Forecasting, and Prediction (Englewood Cliffs, Prentice Hall, 1963).Google Scholar
- 4.D. F. Findley, B. C. Monsell, M. C. Otto, W. R. Bell, and M. Pugh, Towards X-12 ARIMA (Technical report, Bureau of the Census, 1992).Google Scholar
- 5.D. F. Findley, B. C. Monsell, W. R. Bell, M. C. Otto, and B. C. Chen New Capabilities and Methods of the X-12 ARIMA Seasonal Adjustment Program (Technical report, U. S. Bureau of the Census, 1996).Google Scholar
- 7.C. C. Holt, Forecasting Seasonals and Trends by Exponentially Weighted Moving Averages, in ONR Research Memorandum (Carnagie Institute of Technology, Pittsburgh, Pennsylvania, 1957), No.52.Google Scholar