With the gasoline time series data from the given table, show the exponential smoothing forecasts using = 0.1. Week Sales (1000s of gallons) 1 17 2 21 3 19 4 23 5 18 6 16 7 20 8 18 9 22 10 20 11 15 12 22 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. = 0.1 = 0.2 MSE fill in the blank 1 fill in the blank 2 Prefer: 01. or 0.2 2. Are the results the same if you apply MAE as the measure of accuracy? Do not round your interim computations and round your final answers to two decimal places. = 0.1 = 0.2 MAE fill in the blank 4 fill in the blank 5 Prefer: 0.1 or 0.2 3. What are the results if MAPE is used? Do not round your interim computations and round your final answers to two decimal places. = 0.1 = 0.2 MAPE fill in the blank 7 % fill in the blank 8 % Prefer: 0.1 or 0.2
With the gasoline time series data from the given table, show the exponential smoothing forecasts using = 0.1. Week Sales (1000s of gallons) 1 17 2 21 3 19 4 23 5 18 6 16 7 20 8 18 9 22 10 20 11 15 12 22 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. = 0.1 = 0.2 MSE fill in the blank 1 fill in the blank 2 Prefer: 01. or 0.2 2. Are the results the same if you apply MAE as the measure of accuracy? Do not round your interim computations and round your final answers to two decimal places. = 0.1 = 0.2 MAE fill in the blank 4 fill in the blank 5 Prefer: 0.1 or 0.2 3. What are the results if MAPE is used? Do not round your interim computations and round your final answers to two decimal places. = 0.1 = 0.2 MAPE fill in the blank 7 % fill in the blank 8 % Prefer: 0.1 or 0.2
Chapter6: Exponential And Logarithmic Functions
Section6.8: Fitting Exponential Models To Data
Problem 3TI: Table 6 shows the population, in thousands, of harbor seals in the Wadden Sea over the years 1997 to...
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With the gasoline time series data from the given table, show the exponential smoothing forecasts using = 0.1.
Week | Sales (1000s of gallons) |
1 | 17 |
2 | 21 |
3 | 19 |
4 | 23 |
5 | 18 |
6 | 16 |
7 | 20 |
8 | 18 |
9 | 22 |
10 | 20 |
11 | 15 |
12 | 22 |
- 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.
= 0.1 | = 0.2 | |
MSE | fill in the blank 1 | fill in the blank 2 |
Prefer: 01. or 0.2
2. Are the results the same if you apply MAE as the measure of accuracy? Do not round your interim computations and round your final answers to two decimal places.
Prefer: 0.1 or 0.2
= 0.1 | = 0.2 | |
MAE | fill in the blank 4 | fill in the blank 5 |
Prefer: 0.1 or 0.2
3. What are the results if MAPE is used? Do not round your interim computations and round your final answers to two decimal places.
= 0.1 | = 0.2 | |
MAPE | fill in the blank 7 % | fill in the blank 8 % |
Prefer: 0.1 or 0.2
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