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For some scenario variables - such as U.S. real GDP growth, the unemployment rate, and the consumer price index - there will be a large number of different forecasts available to project the paths of these variables in the baseline scenario.
The uncertainty in the forecast can then be assessed by the range of different forecasts produced.
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The second main reason for the differences in forecasts in Fig. 1 is in the use of different forecast models.
Genetic Algorithm (GA) is then used to optimize the weighting coefficients of different forecast components and minimize the forecast error.
First the bullwhip effect induced by the use of different forecasting methods in order-up-to replenishment policies is analysed.
Those predictions are being made through a tool, BestForecast, that analyzes the accuracy of different forecasting models and improves them over time by comparing their predictions with reported conditions.
We proposed a more advanced forecasting system based on two components: a pattern identification procedure, using statistical tests, and a set of different forecasting models, variants of the exponential smoothing.
Here, we analyze the forecasting error of different forecasting methods based on ARIMA model.
(1998) and Witt and Witt (1992) for an appraisal of different forecasting accuracy measures.
The authors also provide a comparative study that highlights the benefits of different forecasting approaches.
Open image in new window Fig. 16 Impact of different forecasting accuracies of the feeder baseline load on room temperature profiles.
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