Consider the following time series data.
Week | 1 | 2 | 3 | 4 | 5 | 6 |
Value | 20 | 13 | 15 | 10 | 17 | 13 |
Using the naïve method (most recent value) as the forecast for the next week, compute the following measures of forecast accuracy.
(c) | Mean absolute percentage error |
If required, round your intermediate calculations and final answer to two decimal places. | |
Time period | Actual Value(A) | Moving avg. Forecast(F) | Forecast error E=|A-F| | Squared Forecast Error | |A-F|A |
1 | 20 | ||||
2 | 13 | 20 | 7 | 49 | 0.5385 |
3 | 15 | 13 | 2 | 4 | 0.1333 |
4 | 10 | 15 | 5 | 25 | 0.5000 |
5 | 17 | 10 | 7 | 49 | 0.4118 |
6 | 13 | 17 | 4 | 16 | 0.3077 |
7 | 13 | ||||
Total | 25 | 143 | 1.89 | ||
Average | 5.00 | 28.60 | 37.83% | ||
MAD | MSE | MAPE | |||
a) mean absolute error= | 5.00 | ||||
b) mean squared error = | 28.60 | ||||
c) mean absolute % error= | 37.83% | ||||
d) forecast for week 7 = | 13 |
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