moving average approach, weighted moving average approach

Gradual, long-term movement in time-series data is called a. seasonal variation b. cycles c. trends d. exponential variation e. random variation Which of the following is not present in a time series? a. seasonality b. operational variations c. trend d. cycles e. random variations The fundamental difference between cycles and seasonality is the a. duration of the repeating patterns b. magnitude of the variation c. ability to attribute the pattern to a cause d. all of the above In time series, which of the following cannot be predicted? a. large increases in demand b. technological trends c. seasonal fluctuations d. random fluctuations e. large decreases in demand What is the approximate forecast for May using a four-month moving average?

Which time series model below assumes that demand in the next period will be equal to the most recent period’s demand? a. naive approach b. moving average approach c. weighted moving average approach d. exponential smoothing approach e. none of the above Which of the following is not a characteristic of simple moving averages? . it smoothes random variations in the data b. it has minimal data storage requirements c. it weights each historical value equally d. it smoothes real variations in the data 21. 3 22. A six-month moving average forecast is better than a three-month moving average forecast if demand a. is rather stable b. has been changing due to recent promotional efforts c. follows a downward trend d. follows an upward trend Increasing the number of periods in a moving average will accomplish greater smoothing, but at the expense of a. manager understanding b. accuracy c. stability d. esponsiveness to changes Which of the following statements comparing the weighted moving average technique and exponential smoothing is true?

Exponential smoothing is more easily used in combination with the Delphi method. b. More emphasis can be placed on recent values using the weighted moving average. c. Exponential smoothing is considerably more difficult to implement on a computer. d. Exponential smoothing typically requires less record-keeping of past data. Which time series model uses past forecasts and past demand data to generate a new forecast? a. naive b. moving average c. weighted moving average d. xponential smoothing Which is not a characteristic of exponential smoothing? a. smoothes random variations in the data b. easily altered weighting scheme c. weights each historical value equally d. has minimal data storage requirements Which of the following smoothing constants would make an exponential smoothing forecast equivalent to a naive forecast?