Consider the following time series data.
Week | 1 | 2 | 3 | 4 | 5 | 6 |
Value | 18 | 12 | 15 | 10 | 18 | 14 |
Using the naïve method (most recent value) as the forecast for the next week, compute the following measures of forecast accuracy.
(a) | Mean absolute error |
If required, round your answer to one decimal place. | |
(b) | Mean squared error |
If required, round your answer to one decimal place. | |
(c) | Mean absolute percentage error |
If required, round your intermediate calculations and final answer to two decimal places. | |
(d) | What is the forecast for week 7? |
Time period | Actual Value(A) | Moving avg. Forecast(F) | Forecast error E=|A-F| | Squared Forecast Error | |A-F|A |
1 | 18 | ||||
2 | 12 | 18 | 6 | 36 | 0.5000 |
3 | 15 | 12 | 3 | 9 | 0.2000 |
4 | 10 | 15 | 5 | 25 | 0.5000 |
5 | 18 | 10 | 8 | 64 | 0.4444 |
6 | 14 | 18 | 4 | 16 | 0.2857 |
7 | 14 | ||||
Total | 26 | 150 | 1.93 | ||
Average | 5.20 | 30.00 | 38.60% | ||
MAD | MSE | MAPE |
a) mean absolute error= | 5.2 | ||
b) mean squared error = | 30.0 | ||
c) mean absolute % error= | 38.60% | ||
d) forecast for week 7 = | 14 |
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