Compute the exponentially smoothed forecast with a = 0.40, the adjusted exponentially smoothed forecast with a = 0.40 and 3 = 0.30, and the linear trend line forecast. Compare the accuracy of the three forecasts using cumulative error and MAD, and indicate which forecast appears to be most accurate.

A security company had to deploy guards for emergencies multiple times in the last four evenings. The numbers of emergencies for Monday, Tuesday, Wednesday, and Thursday were 7, 4, 8, and 11, respectively. What would be the security company's forecast for the number of emergencies on Friday using an exponential smoothing forecasting approach? (Use \alpha= 0.2 and a forecast for Monday of 10 emergencies)

A concert promoter is forecasting this year's attendance for one of his concerts based on the following historical data:   Year Attendance Four Years ago 10,000 Three Years ago 12,000 Two Years ago 18,000 Last Year 20,000   What is this year's forecast using exponential smoothing with alpha = 0.2, if last year's smoothed forecast was 15,000?

Forecasting with exponential smoothing has been compared to driving a car while gazing in the rearview mirror. What circumstances must exist for driving a car that are equivalent to the assumptions made while applying exponential smoothing.

If month one has 240,000 demands, month two 250,080 demands, month three 325,00 demands, month four 370,000 demands, month five 420,000 demands and month six 509,00 demands. Use a forecast for the first month of 240,000, an initial trend forecast of 50,000, and smoothing parameters of 0.35 for both demand smoothing and trend smoothing. Compute the forecasts and trends using double exponential smoothing

The following table shows the actual demand observed over the last 11​ years:                                                                                                                                             Year 1 2 3 4 5 6 7 8 9 10 11 Demand 7 9 6 10 12 7 12 12 9 9 8 Part 2 Using exponential smoothing with   α ​=   0.30 and a forecast for year 1 of   6.0​, provide the forecast from periods 2 through 12 ​(round your responses to one decimal​ place).                                                       Part 3 Provide the forecast from periods 2 through 12 using the naive approach ​(enter your responses as whole​ numbers).

The manager of the Petroco Service Station wants to forecast the demand for unleaded gasoline next month so that the proper number of gallons can be ordered from the distributor. The owner has accumulated the following data on demand for unleaded gasoline from sales during the past 10 months. Compute an exponentially smoothed forecast with an a value of .30. According to the result from Excel and/or POM-QM, the forecast for the month of January would be (type number only, two decimals) Month October November December January February March April May June July 733.25 Gasoline Demanded (gal.) 800 725 630 500 645 690 730 810 1,200 980

Amazon finds that sales in July are much greater relative to other months because of the Amazon Prime days in that month. Assuming that an exponential smoothing model is used for forecasting sales, what should the company best do to make the forecast for the next month (Aug) more reflective of reality? Please pick from the provided options.    F t+1 = αDt + (1-α) Ft     increase value of alpha     decrease value of alpha     stay with the same alpha     decrease the expected forecast for the current month (Ft)     On hearing our forecast, our clients probe - how sure are we about the forecast?  Which measure of errror (among those stated below) should we best use to respond?     a. Root MSE (RMSE)     b. Mean absolute percentage error (MAPE)     c. Mean forecast error (Avg. error)     d. Cumulative forecast error (CFE)

Which of the following are common metrics for measuring the relative quality of different forecasting methods? OAbsolutely Mean Mean Squared Accuracy O Mean Absolute Percent Error Mean Squared Response OMean Squared Error Mean Absolute Error (a.k.a. Mean Absolute Deviation)

In a retail store, the actual sales of a particular product (in thousands of units) over the past few months are as follows: Month Sales 1 16 2 22 3 18 4 20 5 23   Using exponential smoothing method with α (smoothing constant) of 0.75 and the given forecast for month 1 equal to 10, what is the forecast for month 6?