Winter s model

The detector model proposed in Refs. [58, 87] is formally Winter s model complemented by an imaginary absorbing potential. 

Now we show that the source model described above can reproduce Winter s model, that is, we show that Eqs. (125 and 126) give the same probabilities as Eqs. (144 and 143). First, from Eqs. (105 and 139) and from Eqs. (106 and 138) we formulafe fhe connection befween fhe spatial and temporal coordinates,... 

Starting from Eq. (125) and using Eqs. (147 and 149) we may also check thaf the probability obtained from the saddle wave function in the source model reproduces that of Winter s model for strong potentials G >> 1 in the f >> t regime. 

TREND-AND SEASONALITY-CORRECTED EXPONENTIAL SMOOTHING (WINTER S MODEL) This method is appropriate when the systematic component of d and has a level, a trend, and a seasonal factor. In this case we have... 

If Tahoe Salt uses an adaptive forecasting method for the sell-through data obtained from its retailers. Winter s model is the best choice, because its demand experiences both a trend and seasonality. 

Demand in this case clearly has both a trend and seasonality in the systematic component. Thus, the team initially expects Winter s model to produce the best forecast. 

Trend- and Seasonality-Corrected Exponential Smoothing (Winter s Model)... 

It then applies Winter s model with a = 0.05, /3 = 0.1, y = 0.1 to obtain the forecasts. All calculations are shown in Figure 7-10 (see worksheet Figure 7-10). The team makes forecasts using Equation 7.17, updates the level using Equation 7.18, updates the trend using Equation 7.19, and updates seasonal factors using Equation 7.20. 

In this case, MAD = 1,469. Thus, the estimate of standard deviation of forecast error using Winter s model with a = 0.05, j8 = 0.1, and y = 0.1 is 1.25 X 1,469 = 1,836. In this case, the standard deviation of forecast error relative to the demand forecast is much smaller than with the other methods. 

The team compiles the error estimates for the four forecasting methods as shown in Table 7-2. Based on the error information in Table 7-2, the forecasting team decides to use Winter s model. It is not surprising that Winter s model results in the most accurate forecast, because the demand data have both a growth trend as well as seasonality. Using Winter s model, the team forecasts the following demand for the coming four quarters ... 

Consider monthly demand for the ABC Coiporation as shown in Table 7-3. Forecast the monthly demand for Year 6 using moving average, simple exponential smoothing, Holt s model, and Winter s model. In each case, evaluate the bias, TS, MAD, MAPE, and MSE. Which forecasting method do you prefer Why ... 

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