Question

Problem 15-03 (Algorithmic)

Consider the following time series data.

Week 1 2 3 4 5 6

Value 19 14 15 11 19 15

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 (MAE)

b. Mean squared error (MSE)

c. 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 forecast for the next period, compute the same three values. Round your answers to two decimal places.

MAE =

MSE =

MAPE =

Which method appears to provide the more accurate forecasts for the historical data? (Select one)

Naive method (most recent value) OR Average of all the historical data

Thank you so much for your time and help.

Answer 1

ANSWER::

1)

from naive method:

		naïve METHOD
Time period	Actual Value(A)	Moving avg. Forecast(F)	Forecast error E=|A-F|	Squared Forecast Error	|A-F|A
1	19
2	14	19	5	25	0.3571
3	15	14	1	1	0.0667
4	11	15	4	16	0.3636
5	19	11	8	64	0.4211
6	15	19	4	16	0.2667
7		15
		Total	22	122	1.48
		Average	4.40	24.40	29.50%
			MAD	MSE	MAPE

MAE =4.40

MSE =24.40

MAPE =29.50 %

2)

from average of all historical data:

Time period	Actual Value(A)	Moving avg. Forecast(F)	Forecast error E=|A-F|	Squared Forecast Error	|A-F|A
1	19
2	14	19	5	25	0.3571
3	15	16.5	1.5	2.25	0.1000
4	11	16	5	25	0.4545
5	19	14.75	4.25	18.0625	0.2237
6	15	15.6	0.6	0.36	0.0400
7		15.5
		Total	16.35	70.6725	1.18
		Average	3.27	14.13	23.51%
			MAD	MSE	MAPE

MAE =3.27

MSE =14.13

MAPE =23.51 %

3)

average of all the historical data as a forecast for the next period is best as the error is less when compare with naove method

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