Winters, P. R., 'Forecasting Sales by Exponentially Weighted Moving Averages', Management Science, Vol. 60, 1960. Home/Search Document Not in Database Summary Related Articles Check |
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Index Selection In A Self-Adaptive Relational Data Base Management .. - Chan (Correct)
....average strikes a reasonable balance between the two Chapter ,t ,18 Parameters Aoqutsitlon extremes for parameter prediction mntioned .earlier. Porecam. derived. b) weighing past observations exponentially (or geometrica lT)have been used with some suceess in operations research and economi [Brown59, Muth60, Winters60, rown 2] The forecast is based on two sources of evidence, the most recent observation and the forecast made one period before. The exponential smoothing procedure, in its simplest form, is carried out as follows: C4.3) 1) x (1) 4. 4) k) a x(k) 1 ) k 1) where e is called a ....
Winters, P. R., 'Forecasting Sales by Exponentially Weighted Moving Averages', Management Science, Vol. 60, 1960.
Forecasting Uncertain Hotel Room Demand - Mihir Rajopadhye Mounir (Correct)
....This method, however, can be used only for non seasonal time series showing no trend. Due to this, certain adaptations are required in order to use it for time series that arise in real problems. A more general variation of the simple exponential smoothing procedure is the Holt Winters method [9]. The latter considers the local linear trend and seasonality in the data. The trend represents the direction in which the time series is moving, while the seasonality explains the effects of different seasons in the data. This method owes its popularity to the fact that it is very simple to ....
Winters, P. R., Forecasting Sales by Exponentially Weighted Moving Averages, Mgmt. Sci., Vol 6, No 3, (1960) pp 324-342.
System Performance Advisor: An Expert System For Unix System.. - Hoogenboom (1992) (1 citation) (Correct)
....values give more weight to recent values. Thus, the model reacts more quickly to changes. Very large values are to be avoided, however, since the model will react to random fluctuations. The seasonal model described above is one of a family of seasonal models based on a method described by Winters [61]. The model described above is from Montgomery, Johnson, and Gardiner [47] 64 The seasonal model satisfies the five Modeling Requirements discussed in Section 3.2: ffl (time efficiency) mathematically tractable for a large number of hosts, ffl (space efficiency) no need to retain historical ....
....max(oe FE ) Time Series Model for cs User Count 95 good initialization period which is representative of the true behavior of the data. Multiple seasons (in this case, days) of historical data used for the initialization period provide a better initial estimate of the model parameters. Winters [61] says that the response of this type of time series model to changes in ff, fi, and fl is flat : small changes in the parameters have small effects on the quality of the forecasts. The analysis of the models tested here supports that theory. Thus, the benefit of tuning of the model by small ....
Winters, P. R. Forecasting sales by exponentially weighted moving averages. Management Science 6, 3 (April 1960), 324--342.
Combining Forecasting Procedures: Some Theoretical Results - Yang (2000) (Correct)
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Winters, P.R. (1960) Forecasting sales by exponentially weighted moving averages. Man. Sci., 6, 324-342. 29
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