# Consider the following time series data

1. Consider the following time series data. (Use Excel for this problem. Copy paste Excel

Month  1  2  3  4  5  6  7  8  9  10

Value  12  19  24  13  20  23  15  21  23  18

a) Use the Naïve Method of forecasting. What is the forecast for month 11?

b) Use the Forecasting Method that uses the average of all the data available until that

period as the forecast for the next period. What is the forecast for month 11?

c) Which method appears to provide the better forecast? Use different Measures of Forecast

Accuracy in making this determination.

2. Refer to the gasoline sales time series data in Table 18.1 of the Text Book. (Use Excel for

this problem. Copy paste Excel Tables into your report for answering questions).

a) Compute two- week, three-week, and four- week moving average forecasts for the time

series data.

b) Compute Measures of Forecast Accuracy like MAE, MSE, and MAPE.

c) Which method of forecasting of the three (two, three, or four- week moving average

models) would you prefer to use for future forecasting purposes? Justify your answer.

3. For the Hawkins Company, the monthly percentages of all shipments received on time over

the past 12 months are: 99, 104, 101, 98, 100, 104, 101, 102, 98, 100, 101, and 102.

a) Construct a time series plot. What type of pattern exists in the data?

b) Compare the three- month moving average approach and exponential smoothing

approach for α = .3. Which method provides a better forecasting model?

c) What is the forecast for next month in each case?

4. Consider the following time series.

t  1  2  3  4  5  6  7  8  9  10

Yt  120 110 100  96  94  92  88  84  82  78

a) Construct a time series plot. What type of pattern exists in the data?

b) Develop the linear trend equation. What are the values for slope and intercept?

c) What is the forecast for t = 11?

d) Is the linear trend statistically significant? Use α = .05

e) What is the Goodness of fit measure (strength of this linear trend) is?

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