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Forecasting Problems

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5-12
Develop a four-month moving average forecast for Garden Wallace Supply and compute the MAD. A three-month moving average forecast was developed in the section on the moving averages in Table 5.3.

5-13
Using MAD, determine whether the forecast in Problem 5-12 or the forecast in the section concerning Wallace Garden Supply is more accurate.

5-14
Data collected on the yearly demand for 50-pound bags of fertilizer at Wallace Garden Supply are shown in the following table. Develop a three-year moving average to forecast sales. Then estimate demand again with a weighted moving average in which sales in the most recent year are given a weight of 2 and sales in the other two years are each given the weight of 1. Which method do you think is best?

YEAR DEMAND FOR FERTILIZER
(1,000S OF BAGS)
1 4
2 6
3 4
4 5
5 10
6 8
7 7
8 9
9 12
10 14
11 15

5-18
Sales of Cool-Man air conditioners have grown steadily during the past five years.

YEAR SALES
1 450

2 495

3 518

4 563

5 584

6 ?

The sales manager had predicted, before the business started, that year 1's sales would be 410 air conditioners. Using the exponential smoothing with a weight of = 0.30, develop forecasts for years 2 through 6.

5-22
Using the trend projection method, develop a forecasting model for the sales of Cool-Man air conditioners (see problem 5-18).

5-33
Management of Davis's Department Store has used time-series extrapolation to forecast retail sales for the next four quarters. The sales estimates are $100,000, $120,000, $140,000, and $160,000 for the respective quarters before adjusting for seasonality. Seasonal indices for the four quarters have been found to be 1.30, 0.90, 0.70, and 1.10, respectively. Compute a seasonalized or adjusted sales forecast.

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Solution Summary

This posting provides solution to several forecasting problems including moving average, exponential smoothing, trend analysis, weighted moving average etc.

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Time series forecasting problem

Problem (see attached file)

we are going time series techniques to predict non-retail sales for the next three months. Use the data in the file non retail time series for these exercises.

1. From the graph of the non retail sales data does there appear to be a seasonal component? If yes, what number of seasons do there appear to be in each set?

2. Compare the MSD for forecasts using Trend Analysis with that for Decomposition. What conclusion do you draw?

3. What are the values of the seasonal indices for decomposition? Interpret these values. Do they make sense with respect to the graph in question 1?

4. What are the forecast values for months 13,14, and 15?

5. Can you forecast for month's 16, 17, and 18? Comment on the forecasts with respect to potential accuracy.

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