Exam 16: Analyzing and Forecasting Time-Series Data

A company has developed a linear trend regression model based on 16 quarters of data. The independent variable is the measure of time (t = 1 thru 16 where quarter 1 is winter quarter, 2 is spring, etc.). The company has also developed seasonal indexes for each quarter as follows: The linear trend forecast equation is: = 120 + 56t. Given this information, which of the four quarters beginning next winter (period t = 17) will have the highest seasonally adjusted forecast?

One of the basic tools for creating a trend-based forecasting model is regression analysis.

When using the multiplicative time-series model to determine the seasonal indexes, the first step is to isolate the seasonal and random components from the cyclical and trend components.

Prior to conducting double exponential smoothing a simple linear regression is conducted and the trend equation is = 42 + 38.3t, so the smoothed constant process value should be C0 = 38.3 and the smoothed trend value should be T0 = 42.

A company has recorded 12 months of sales data for the past year and has found the linear trend equation is Based on this information, which of the following is the forecast for period 13?

Stock analysts have recently stated in a meeting on Wall Street that over the past 50 years there have been periods of high market prices followed by periods of lower prices but over time prices have moved upwards. Given their statement, stock prices most likely exhibit only trend and cyclical components.

If a company has 12 years of annual sales data and is in need of a three-year forecast, which of the following forecasting techniques might be useful?

Two common unweighted indexes are the Paasche Index and the Laspeyres Index.

In a double smoothing model, large values for the two smoothing constants will result in greater smoothing of the time series.

An advantage of exponential smoothing techniques over a regression-based trend model is that the exponential smoothing model allows us to weigh each observation equally, thereby giving a fairer method of developing a forecast.

A scatter plot of time series data with time on the vertical axis is an effective means of assessing whether the series is linear or nonlinear.

The most useful tool to identify the trend component of a time series is

A company has developed a linear trend regression model based on 16 quarters of data. The independent variable is the measure of time (t = 1 thru 16 where quarter 1 is winter quarter, 2 is spring, etc.). The company has also developed seasonal indexes for each quarter as follows: The linear trend forecast equation is: = 120 + 56t. Given this information, what is the seasonally adjusted forecast for period 19?

Large values of the Durbin-Watson d statistic indicate that positive autocorrelation among the forecast errors exists.

To deseasonalize a time series, assuming a multiplicative model, the observed values are divided by the appropriate seasonal index.

Given the following time series data: and using smoothing constants of alpha = 0.3, the exponential smoothing forecast for time period 3 is

Recently, a manager for a major retailer computed the following seasonal indexes: The manager then developed the following least squares trend model based on the past five years of quarterly data: = 200 + 11.5t. Based on this, the seasonally adjusted forecast for quarter 25, which is the winter quarter, is 489.11

Under which of the following conditions would you suggest that a double exponential smoothing model should be used instead of a single exponential smoothing model?

A seasonal index is a statistic that is computed from time-series data to indicate the effect of the seasonality in the time-series data.

A company has developed a linear trend model to forecast monthly sales. The following data show the actual sales and the "fitted" sales for months 1-12. Based on these data, what is the mean square error for the linear trend model?