Exam 17: Time Series Analysis and Forecasting

Exhibit 17-3 Consider the following time series. -Regarding a regression model, all of the following can be negative except the

The time series pattern that reflects repeating variability within a single year is called the

Actual sales for January through April are shown below. Use exponential smoothing with $\alpha$ = 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.

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

The actual demand for a product and the forecast for the product are shown below. Calculate MAD and MSE. Show all of your computations.

A parameter of the exponential smoothing model which provides the weight given to the most recent time series value in the calculation of the forecast value is known as the

All of the following are true about a stationary time series except

All of the following are true about a cyclical pattern except

All of the following are true about qualitative forecasting methods except

All of the following are true about time series methods except

Below you are given information on crime statistics for Middletown. The seasonal factors for these data are a.Deseasonalize the series. b.Obtain an estimate of the linear trend for this series. c.Use the seasonal and trend components to forecast the number of crimes for each quarter of Year 5.

Using exponential smoothing, the demand forecast for time period 10 equals the demand forecast for time period 9 plus

Given an actual demand of 61, forecast of 58, and an $\alpha$ of .3, what would the forecast for the next period be using simple exponential smoothing?

A method that uses a weighted average of past values for arriving at smoothed time series values is known as

The number of new central air conditioning systems installed by CoolBreeze, Inc. in each of the last nine years is listed below. Assuming a linear trend function, forecast the number of system installations CoolBreeze will perform in 2014 using linear trend regression.

The sales records of a major auto manufacturer over the past ten years are shown below. Develop a linear trend expression and project the sales (the number of cars sold) for time period t = 11

A group of observations measured at successive time intervals is known as

Exhibit 17-2 Consider the following time series. -Refer to Exhibit 17-2. The forecast for period 10 is

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.

Consider the sales for six consecutive weeks for Sam's Strawberries. The sales are in "flats" sold.Week Sales 1 16 2 18 3 14 4 10 5 20 6 22 a. Using a moving average with AP = 3, forecast the sales for weeks four through six. b. Use a weighted moving average with weights of .5 (most recent), .4, and .1 (oldest) to predict the sales for weeks four through six. c. Use the naïve approach to predict the sales for weeks four through six. d. Use exponential smoothing with $\alpha$ = .3 to forecast sales for weeks four through six.