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Posted: December 16th, 2022
Consider the following time series data
1. Consider the following time series data. (Use Excel for this problem. Copy paste Excel
Tables into your report for answering questions).
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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