Exam 17: Time Series Analysis and Forecasting

In the linear trend equation, T = b₀ + b₁t, b₁ represents the

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.

For the following time series data, using the naïve method (the most recent value as the forecast for the next period), compute the following measures of forecast accuracy. a Mean absolute error (MAE) b.Mean squared error (MSE) c.What is the forecast for period 7?

Consider the following time series. a.Develop a linear trend equation for this time series. b.What is the forecast for t = 17?

Exhibit 18-3 Consider the following time series. -Refer to Exhibit 18-3. The intercept, b₀, is

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

In the linear trend equation, T = b₀ + b_1t, b₀ represents the

The time series component which reflects a regular, multi-year pattern of being above and below the trend line is

Which of the following is not present in a time series?

What is the forecast for July based on a three-month weighted moving average applied to the following past demand data and using the weights: 5, 4, and 3 (largest weight is for most recent data)? Show all of your computations for April through July.

A component of the time series model that results in the multi-period above-trend and below-trend behavior of a time series is

Student enrollment at a university over the past six years is given below. a.Develop a linear trend expression for the above time series. b.Forecast enrollment for year 10.

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

Exhibit 18-1 Below you are given the first five values of a quarterly time series. The multiplicative model is appropriate and a four-quarter moving average will be used. -Refer to Exhibit 18-1. An estimate of the seasonal-irregular component for Quarter 3 of Year 1 is

The following data show the quarterly sales of a major auto manufacturer (introduced in exercise 4) for the years 8 through 10. a.Compute the four-quarter moving average values for the above time series. b.Compute the seasonal factors for the four quarters. c.Use the seasonal factors developed in Part b to adjust the forecast for the effect of season for year 9.

Exhibit 18-1 Below you are given the first five values of a quarterly time series. The multiplicative model is appropriate and a four-quarter moving average will be used. -Refer to Exhibit 18-1. An estimate of the trend component times the cyclical component (T₂C_t) for Quarter 3 of Year 1, when a four-quarter moving average is used, is

The trend component is easy to identify by using

The time series component that reflects variability due to natural disasters is called

The quarterly sales of a company (in millions of dollars) over the past three years are given in the following table. a.Compute the four seasonal factors (Seasonal Indexes). Show all of your computations. b.The trend for these data is Trend = 20.82 + 0.336 t (t represents time, where t=1 for Quarter 1 of 2007 and t=12 for Quarter 4 of 2009). Forecast sales for the first quarter of 2010 using the trend only. Show all of your computations. c.Forecast sales for the first quarter of 2010 using the trend and seasonal indexes and write your answer below. Show all of your computations.

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.