following table displays weekly sales of gasoline in 1000s of gallons of a fuel station use exponential smoothing with alpha 0.2
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If you set the smoothing radius to 250km, and view a polar map, you can clearly see the effect of the South Pole observations on all 40 of the southernmost grid cells. Oddly (and I don’t think this is right at all), if the smoothing radius is set to 1200km, the influence of the south pole base also seems to extend into the second row of grid
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With the gasoline time series data from Question 7 , show the exponential smoothing forecasts using x = 0.1. a. Applying the MSE measure of forecast accuracy, would you prefer a smoothing constant of x = 0.1 or x = 0.2 for the gas sales time series? b. Are the results the same if you apply MAE as the measure of accuracy? c.
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Table 24 9 The table below lists the per gallon prices of gas and milk for the months of April, May, and June. Assume that the typical consumer buys 60 gallons of gas and 4 gallons of milk each month, and that April is the base period. Month Price of Gas Price of Milk April $ $ May $ $ June $ $ Refer to Table 24 9.
Question 3 Data collected on the gasoline sales over the
Data collected on the gasoline sales over the past 12 weeks are shown in the following table. a) Moving Average Method. Sales(1000s of.
(Solved) Use the gasoline sales time series data from
Refer to the gasoline sales time series data in the given table. Week Sales (1000s of gallons) 1 17 2 21 3 19 4 23 5 17 6 16 7 20 8 19 9 21 10 19 11 15 12 23 Compute four week and five week moving Posted one year ago
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15 4 Chapter 15 Time Series Analysis and Forecasting Sales (1000s of gallons) 0 20 15 10 5 0 479 Week 25 12 3 65 8 10 1211 FIGURE 15.1 GASOLINE SALES TIME SERIES PLOT 2For a formal definition of stationary, see G. E. P., Box, G. M. Jenkins, and G. C. Reinsell, Time Series Analysis: Forecasting and Control, 3rd ed. Englewood Cliffs, NJ: Prentice Hall, 1994, p. 23.
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With the gasoline time series data from the given table, show the exponential smoothing forecasts using = 0.1. Applying the MSE measure of forecast accuracy, would you prefer a smoothing constant of = 0.1 or = 0.2 for the gasoline sales time series? Do not round your interim computations and round your final answers to two decimal places.
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