Consider the following time series data.Week1346.Value181316111714a. Construct a time series plot. What type of pattern exists in the data? (b. Develop the three-week moving average forecasts for this time series. Compute MSE and aforecast for week 7. (c. Use a = .2 to compute the exponential smoothing forecasts for the time series. Compute MSE and aforecast for week 7. .d. Compare the three-week moving average approach with the exponential smoothing approach usinga = .2. Which appears to provide more accurate forecasts based on MSE? Explain.e. Use a smoothing constant of a = .4 to compute the exponential smoothing forecasts. Does asmoothing constant of .2 or .4 appear to provide more accurate forecasts based on MSE? Explain.

Here, we have the table data that represent time-series data, we have data values over some periods,…

a. Construct a time series plot. What type of pattern exists in the data? ( b. Develop the three-week moving average forecasts for this time series. Compute MSE and a forecast for week 7. ( c. Use a = .2 to compute the exponential smoothing forecasts for the time series. Compute MSE and forecast for week 7. .

a. Construct a time series plot. What type of pattern exists in the data? ( b. Develop the three-week moving average forecasts for this time series. Compute MSE and a forecast for week 7. ( c. Use a = .2 to compute the exponential smoothing forecasts for the time series. Compute MSE and forecast for week 7. .

Practical Management Science

6th Edition

ISBN:9781337406659

Author:WINSTON, Wayne L.

Publisher:WINSTON, Wayne L.

Chapter13: Regression And Forecasting Models

Section13.7: Exponential Smoothing Models

Problem 26P: The file P13_26.xlsx contains the monthly number of airline tickets sold by the CareFree Travel...

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The file P13_42.xlsx contains monthly data on consumer revolving credit (in millions of dollars) through credit unions. a. Use these data to forecast consumer revolving credit through credit unions for the next 12 months. Do it in two ways. First, fit an exponential trend to the series. Second, use Holts method with optimized smoothing constants. b. Which of these two methods appears to provide the best forecasts? Answer by comparing their MAPE values.
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The file P13_26.xlsx contains the monthly number of airline tickets sold by the CareFree Travel Agency. a. Create a time series chart of the data. Based on what you see, which of the exponential smoothing models do you think will provide the best forecasting model? Why? b. Use simple exponential smoothing to forecast these data, using a smoothing constant of 0.1. c. Repeat part b, but search for the smoothing constant that makes RMSE as small as possible. Does it make much of an improvement over the model in part b?
The file P13_29.xlsx contains monthly time series data for total U.S. retail sales of building materials (which includes retail sales of building materials, hardware and garden supply stores, and mobile home dealers). a. Is seasonality present in these data? If so, characterize the seasonality pattern. b. Use Winters method to forecast this series with smoothing constants = = 0.1 and = 0.3. Does the forecast series seem to track the seasonal pattern well? What are your forecasts for the next 12 months?
The file P13_02.xlsx contains five years of monthly data on sales (number of units sold) for a particular company. The company suspects that except for random noise, its sales are growing by a constant percentage each month and will continue to do so for at least the near future. a. Explain briefly whether the plot of the series visually supports the companys suspicion. b. By what percentage are sales increasing each month? c. What is the MAPE for the forecast model in part b? In words, what does it measure? Considering its magnitude, does the model seem to be doing a good job? d. In words, how does the model make forecasts for future months? Specifically, given the forecast value for the last month in the data set, what simple arithmetic could you use to obtain forecasts for the next few months?
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