Home / Mathematics /Problem 6-09 With the gasoline time series data from the given table, show the exponential smoothing forecasts using α-0.1. GASOLINE SALES TIME SERIES Sales (1000s of gallons) 17 21 23 18 16 20 18 10 20 15 12 (a) Applying the MSE measure of forecast accuracy, would you prefer a smoothing constant of α-0.1 or α-0.2 for the gasoline sales time series? Do not Problem 6-09 With the gasoline time series data from the given table, show the exponential smoothing forecasts using α-0.1. GASOLINE SALES TIME SERIES Sales (1000s of gallons) 17 21 23 18 16 20 18 10 20 15 12 (a) Applying the MSE measure of forecast accuracy, would you prefer a smoothing constant of α-0.1 or α-0.2 for the gasoline sales time series? Do not round your interim computations and round your final answers to three decimal places. a 0.1 α=0.2 MSE Prefe- Select your answer- 0.1 (b) Aret 0.2 u apply MAE as the measure of accuracy?

Question Problem 6-09 With the gasoline time series data from the given table, show the exponential smoothing forecasts using α-0.1. GASOLINE SALES TIME SERIES Sales (1000s of gallons) 17 21 23 18 16 20 18 10 20 15 12 (a) Applying the MSE measure of forecast accuracy, would you prefer a smoothing constant of α-0.1 or α-0.2 for the gasoline sales time series? Do not Problem 6-09 With the gasoline time series data from the given table, show the exponential smoothing forecasts using α-0.1. GASOLINE SALES TIME SERIES Sales (1000s of gallons) 17 21 23 18 16 20 18 10 20 15 12 (a) Applying the MSE measure of forecast accuracy, would you prefer a smoothing constant of α-0.1 or α-0.2 for the gasoline sales time series? Do not round your interim computations and round your final answers to three decimal places. a 0.1 α=0.2 MSE Prefe- Select your answer- 0.1 (b) Aret 0.2 u apply MAE as the measure of accuracy?

RCQYEL The Asker · Probability and Statistics

Transcribed Image Text: Problem 6-09 With the gasoline time series data from the given table, show the exponential smoothing forecasts using α-0.1. GASOLINE SALES TIME SERIES Sales (1000s of gallons) 17 21 23 18 16 20 18 10 20 15 12 (a) Applying the MSE measure of forecast accuracy, would you prefer a smoothing constant of α-0.1 or α-0.2 for the gasoline sales time series? Do not round your interim computations and round your final answers to three decimal places. a 0.1 α=0.2 MSE Prefe- Select your answer- 0.1 (b) Aret 0.2 u apply MAE as the measure of accuracy?
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Transcribed Image Text: Problem 6-09 With the gasoline time series data from the given table, show the exponential smoothing forecasts using α-0.1. GASOLINE SALES TIME SERIES Sales (1000s of gallons) 17 21 23 18 16 20 18 10 20 15 12 (a) Applying the MSE measure of forecast accuracy, would you prefer a smoothing constant of α-0.1 or α-0.2 for the gasoline sales time series? Do not round your interim computations and round your final answers to three decimal places. a 0.1 α=0.2 MSE Prefe- Select your answer- 0.1 (b) Aret 0.2 u apply MAE as the measure of accuracy?
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Please find below table with corresponding filled up data .Following may be noted :Formula for Forecasted demand : Ft = alpha x Ft-1 + ( 1 – alpha) x St-1Ft = Forecasted demand for period tFt-1 = Forecasted demand for period t-1St-1 = Actual sales value for period t-1Alpha = smoothing constant ( which can be either 0.1 or 0.2 anddata have been plotted basis both these values of alpha)Absolute error for period t = Absolute difference ( Forecastedsales value for period t – Actual sales value for period t)Squared error for period t = ( Absolute error for periodt)^2Absolute percentage error for period t = Absolute error forperiod t / Sales value for period t     x100  WEEKSales ( 1000 of Gallons)Forecast ( smoothing constant = alpha = 0.1)Squared errorAbsolute errorAbsolute percentage errorForecast ( smoothing constant = 0.2)Squared errorAbsolute errorAbsolute percentage error117170.00001700.0002211716.00419.0517164.0019.0531917.402.561.608.4217.801.441.206.3242317.5629.595.4423.6518.0424.604.9621.5751818.100.010.100.5819.031.071.035.7361618.094.382.0913.0918.837.982.8317.6672017.884.482.1210.5818.263.031.748.7081818.100.010.100.5318.610.370.613.3892218.0915.323.9117.7918.4912.343.5115.97102018.482.321.527.6119.190.660.814.05111518.6313.183.6324.2019.3518.944.3529.01122218.2713.943.7316.9718.4812.383.5215.99  SUM=101.7828.25142.46&#1 ... See the full answer

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