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Recognized from its final versions because the "Bible of Forecasting", the 3rd variation of this authoritative textual content has followed a brand new approach-one that's as new because the most recent tendencies within the box: "Explaining the previous isn't enough for predicting the future". In different phrases, exact forecasting calls for greater than simply the appropriate of types to ancient information. inside of, readers will locate the newest innovations utilized by managers in company at the present time, observe the significance of forecasting and find out how it is entire. And readers will strengthen the required abilities to fulfill the elevated call for for considerate and sensible forecasts.
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Additional info for Forecasting: Methods and Applications
Another. 18). Table 2-10 shows how to compute the PE, MPE, and MAPE measures. From the point of view of the ultimate user of forecasting, knowing that the MAPE of a method is 5% means a great deal more than simply knowing that the MSE is 183. However, the MAPE is only meaningful if the scale has a meaningful origin. For example, one would not use MAPE for assessing the accuracy of temperature forecasting since the common temperature scales (Fahrenheit and Celsius) have fairly arbitrary zero points.
57 80 60 40 20 0 Mean squared error (MSE) 100 2/6 Least squares estimates 0 5 10 15 20 Estimate value Figure 2-8: Relating MSE to estimate value. Because Figure 2-8 is a mathematical function whose properties can be found exactly, it is not necessary to use trial and error to find the estimator that minimizes the MSE. Rather, this value can be found mathematically with the help of differentiation. 21) so as to isolate the error on the left-hand side: error = data − pattern. 22) As usual, the error will be denoted by e, the data by Y , and the estimate pattern by Yˆ .
Table 2-12 shows how to compute Theil’s U -statistic for the beer data. Theil’s U -statistic can be better understood by examining its 50 Chapter 2. Basic Forecasting Tools interpretation. 19) will be 0 only if FPEt+1 = APEt+1 for t = 1, 2, . . , n−1. That in turn occurs only when the forecasts are exact (give 0 error). Alternatively, the U -statistic will have a value of 1 only when FPEt+1 is equal to 0. That would be the case only if the errors in the forecasting method were the same as those that would be obtained by forecasting no change at all in the actual values.