Problem 15-03 (Algorithmic) Consider the following time series data. Using the n

Question

Problem 15-03 (Algorithmic)

Consider the following time series data.

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 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? Explain.

Week 1 2 3 4 5 6 Value 20 14 15 10 17 14

Explanation / Answer

I am attaching the tables here for the forecasted values.

Mean absolute error (MAE) = (6 + 1 + 5 + 7 + 3)/5 = 4.40

Mean squared error (MSE) = (62 +12 +52 + 72 + 32 )/5 = 24.00

Mean absolute percentage error (MAPE) = The calculation are in table

MAPE = 100/n (V-F)/V) = (100/5) * 1.621 = 32.42%

(b) Now part II where we will take average of all historical data

Mean absolute error (MAE) = (6 + 2 + 6.33 + 2.25 + 1.2)/5 = 3.56

Mean squared error (MSE) = (62 +22 +6.332 + 2.252 + 1.22 )/5 = 17.32

Mean absolute percentage error (MAPE) = The calculation are in table

MAPE = 100/n (V-F)/V) = (100/5) * 1.413= 28.26%

The method two appears more accurate among two forecast methods.

Week Value (V) Forecast (F) Abs Error 1 20 2 14 20 6 3 15 14 1 4 10 15 5 5 17 10 7 6 14 17 3 7 14

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