You are not currently logged in.
Access JSTOR through your library or other institution:
Improving Demand Forecasting Accuracy Using Nonlinear Programming Software
J. D. Bermúdez, J. V. Segura and E. Vercher
The Journal of the Operational Research Society
Vol. 57, No. 1 (Jan., 2006), pp. 94-100
Stable URL: http://www.jstor.org/stable/4102339
Page Count: 7
You can always find the topics here!Topics: Time series forecasting, Forecasting techniques, Analytical forecasting, Forecasting models, Data smoothing, Spreadsheets, A posteriori knowledge, Sales forecasting, Statistical forecasts, Zero
Were these topics helpful?See somethings inaccurate? Let us know!
Select the topics that are inaccurate.
Preview not available
We address the problem of forecasting real time series with a proportion of zero values and a great variability among the nonzero values. In order to calculate forecasts for a time series, the model coefficients must be estimated. The appropriate choice of values for the smoothing parameters in exponential smoothing methods relies on the minimization of the fitting errors of historical data. We adapt the generalized Holt-Winters formulation so that it can consider the starting values of the local components of level, trend and seasonality as decision variables of the nonlinear programming problem associated with this forecasting procedure. A spreadsheet model is used to solve the problems of optimization efficiently. We show that our approach produces accurate forecasts with little data per product.
The Journal of the Operational Research Society © 2006 Operational Research Society