for exponential smoothing: next period forecast =α*last period actual+(1-α)*last period forecast |
exponential smoothing | ||
month | value | forecast |
1 | 17 | |
2 | 21 | 17.00 |
3 | 19 | 17.40 |
4 | 23 | 17.56 |
5 | 18 | 18.10 |
6 | 16 | 18.09 |
7 | 20 | 17.88 |
8 | 18 | 18.10 |
9 | 22 | 18.09 |
10 | 20 | 18.48 |
11 | 15 | 18.63 |
12 | 22 | 18.27 |
a)
b)
α=0.1 provides more accurate forecasts based upon MAE , so the results are not the same
c)
α =0.1 provides more accurate forecasts based upon MAPE
Please help. I'm stuck. DATA: Gassins These data show the number of palons of gasoline sold...
use the following gasoline sales time series. If needed, round your answers to two-decimal digits. Week Sales (1,000s of gallons) 0.1, and a -0.2. (a) Show the exponential smoothing forecasts using a Exponential Smoothing Week a = 0.1 -0.2 (b) Applying the MSE measure of forecast accuracy, would you prefer a smoothing constant of a 0.1 or 0.2 for the gasoline sales time series? An - Select your answer- smoothing constant provides the more accurate forecast, with an overall MSE...
business analytics
Consider the following gasoline sales time series. If needed, round your answers to two-decimal digits. Week Sales (1,000s of gallons) (a) Show the exponential smoothing forecasts using a -0.1, and 0.2 Exponential Smoothing 9 = 0.1 0 = 0.2 Week (b) Applying the MSE measure of forecast accuracy, would you prefer a smoothing constant of a 0.1 or 0 - 0.2 for the gasoline sales time series? An - Select your answer- smoothing constant provides the more accurate...
Consider the following gasoline sales time series. If needed, round your answers to two-decimal digits. Week Sales (1,000s of gallons) 1 17 2 21 3 16 4 24 5 17 6 18 7 22 8 20 9 21 10 19 11 16 12 25 (a) Show the exponential smoothing forecasts using α = 0.1, and α = 0.2. Exponential Smoothing Week α = 0.1 α = 0.2 13 (b) Applying the MSE measure of forecast accuracy, would you prefer a...
GASOLINE SALES TIME SERIES Sales (1000s of gallons) Week 21 19 5 18 6 16 7 20 18 22 10 20 11 15 12 22 show the exponential smoothing forecasts using a = 0.1 a. Applying the MSE measure of forecast accuracy, would you prefer a smoothing constant of a 2 for the gasoline sales time series (to 2 decimals)? .1 or a MSE for a = 1 MSE for a = 2 Select b. Are the results the same...
The options for prefer for A B and C are
0.1 or 0.2
With the gasoline time series data from the given table, show the exponential smoothing forecasts using a = 0.1. GASOLINE SALES TIME SERIES Week 1 2 3 4 5 6 7 8 9 10 11 12 Sales (1000s of gallons) 17 21 19 23 18 16 20 18 22 20 15 22 a. Applying the MSE measure of forecast accuracy, would you prefer a smoothing constant of...
Please help
Consider the following time series data. Week 1 N 3 4 5 6 Value 19 11 13 10 14 12 (a) Construct a time series plot. 20 18 20 18 14 12 10 Week 3 4 Week D 20 18+ 16 Time Series Value Time Series Value 5 Week 0 Wook What type of pattem exists in the data? The data appear to follow a cyclical pattern. The data appear to follow a trend pattem. The data appear...
Consider the following time series data. Week 1 2 3 4 5 6 Value 19 11 16 1017 15 (a) Construct a time series plot. 20 20 20 18 16 14 12 10 c 14 12 12 0 23 4 5 67 0 23 4 5 67 Week Weck Week 20 18 0 1 2345 6 7 Week What type of pattern exists in the data? The data appear to follow a seasonal pattern. The data appear to follow a...
Problem 15-03 (Algorithmic) Consider the following time series data. Week 1 2 3 4 5 6 Value 18 14 16 11 17 13 Using the naïve method (most recent value) as the forecast for the next week, compute the following measures of forecast accuracy: Mean absolute error (MAE) Mean squared error (MSE) Mean absolute percentage error (MAPE) Round your answers to two decimal places. MAE = MSE = MAPE = Using the average of all the historical data as a...
omework Consider the following time series data Month 1 2 3 4 5 6 7 Value 21 14 18 13 18 21 14 a. Which of the following is a correct time series plot for this data? や" -Select your answer- What type of pattern exists in the data? -select your answer- b. Develop the three-month moving average forecasts for this time series. Compute MSE and a forecast for month 8 (to 2 decimals if necessary). Enter negative values as...
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Problem 15-03 (Algorithmic) Consider the following time series data. Week 1 2 3 4 5 6 Value 1914 16 10 17 15 Using the naive method (most recent value) as the forecast for the next week, compute the following measures of forecast accuracy: a. Mean absolute error (MAE) b. Mean squared error (MSE) c. Mean absolute percentage error...