Exam 17: Time Series Analysis and Forecasting

The following time series shows the number of units of a particular product sold over the past six months. a.Compute a 3-month moving average (centered) for the above time series. b.Compute the mean square error (MSE) for the 3-month moving average. c.Use $\alpha$ = 0.2 to compute the exponential smoothing values for the time series. d.Forecast the sales volume for month 7.

The following time series gives the number of units sold during 5 years at a boat dealership. a. Find the four-quarter centered moving averages. b. Plot the series and the moving averages on a graph. c. Compute the seasonal-irregular component. d. Compute the seasonal factors for all four quarters. e. Compute the deseasonalized time series for sales. f. Calculate the linear trend from the deseasonalized sales. g. Forecast the number of units sold in each quarter of year 6.

The temperature in Chicago has been recorded for the past seven days. You are given the information below. a.Produce exponential smoothing forecasts for the series using a smoothing constant of .2. b.Compute the mean square error for the forecasts produced with a smoothing constant of .2. c.What is the forecasted temperature for day 8? d.Is a smoothing constant of .2 or .3 better for the temperature data? Explain.

Below you are given the first four values of a time series. Using a 4-period moving average, the forecasted value for period 5 is

The model that assumes that the actual time series value is the product of its components is the

Actual sales for January through April are shown below. Use exponential smoothing with = 0.3 to calculate smoothed values and forecast sales for May from the above data. Assume the forecast for the initial period (January) is 18. Show all of your computations from February through May.

Which of the following smoothing constants would make an exponential smoothing forecast equivalent to a naive forecast?

Demand for a product and the forecasting department's forecast (naïve model) for a product are shown below. a.Compute the mean absolute error. b.Compute the mean squared error.

Actual sales for January through April are shown below. Use exponential smoothing with = 0.2 to calculate smoothed averages and forecast sales for May from the above data. Assume the forecast for the initial period (January) is 18. Show all of your computations.

John has collected the following information on the amount of tips he has collected from parking cars the last seven nights. a.Compute the 3-day moving averages for the time series. b.Compute the mean square error for the forecasts. c.Compute the mean absolute deviation for the forecasts. d.Forecast John's tips for day 7.

The time series component that reflects gradual variability over a long time period is called

Actual sales of a company (in millions of dollars) for January through April are shown below. a.Use = 0.3 to compute the exponential smoothing values for sales. Compute MSE and forecast sales for May. Show all of your computations from February through May. b.Use = 0.1 to compute the exponential smoothing values for sales. Compute MSE and forecast sales for May. Show all of your computations from February through May. c.Based on MSE, which provides a better forecast? Explain why?

The time series component that reflects variability during a single year is called

Exhibit 18-2 Consider the following time series. -Refer to Exhibit 18-2. The intercept, b₀, is

Exhibit 18-2 Consider the following time series. -Refer to Exhibit 18-2. The slope of linear trend equation, b₁, is

The sales volumes of CMM, Inc., a computer firm, for the past 8 years is given below. a.Develop a linear trend expression for the above time series. b.Forecast sales for period 9.

Consider the following annual series on the number of people assisted by a county human resources department. a.Prepare 3-year moving average values to be used as forecasts for periods 4 through 11. Calculate the mean squared error (MSE) measure of forecast accuracy for periods 4 through 11. b.Use a smoothing constant of .4 to compute exponential smoothing values to be used as forecasts for periods 2 through 11. Calculate the MSE. c.Compare the results in Parts a and

If the estimate of the trend component is 158.2, the estimate of the seasonal component is 94%, the estimate of the cyclical component is 105%, and the estimate of the irregular component is 98%, then the multiplicative model will produce a forecast of

Below you are given the seasonal factors and the estimated trend equation for a time series. These values were computed on the basis of 5 years of quarterly data. T = 126.23 - 1.6t Produce forecasts for all four quarters of year 6 by using the seasonal and trend components.

Exhibit 18-2 Consider the following time series. -Refer to Exhibit 18-2. The forecast for period 5 is