Exam 5: Forecasting

Time series models rely on judgment in an attempt to incorporate qualitative or subjective factors into the forecasting model.

Explain, briefly, why, in the exponential smoothing forecasting method, the larger the value of the smoothing constant, α, the better the forecast will be in allowing the user to see rapid changes in the variable of interest.

An advantage of exponential smoothing over a simple moving average is that exponential smoothing requires one to retain less data.

The trend component of a time series captures whether the level of the variable of interest is generally increasing or decreasing over time.

In general terms, describe the difference between a general linear regression model and a trend projection.

A trend-projection forecasting method is a causal forecasting method.

Using the additive decomposition model, what would be the period 2, Q3 forecast using the following equation: = 20 + 3.2X₁ + 1.5X₂ + 0.8X₃ + 0.6X₄?

In general terms, describe what causal forecasting models are.

Use simple exponential smoothing with α = 0.7 to forecast llama sales for February through May.Assume that the forecast for January was for 22 llamas.

Daily demand for newspapers for the last 10 days has been as follows: 12, 13, 16, 15, 12, 18, 14, 12, 13, 15 (listed from oldest to most recent).Forecast sales for the next day using a two-day moving average.

In picking the smoothing constant for an exponential smoothing model, we should look for a value that

Briefly describe why the scatter diagram is helpful.

In a weighted moving average, the weights assigned must sum to 1.

Average starting salaries for students using a placement service at a university have been steadily increasing.A study of the last four graduating classes indicates the following average salaries: $60,000, $72,000, $84,500, and $96,000 (last graduating class).Predict the starting salary for the next graduating class using a simple exponential smoothing model with α = 0.25.Assume that the initial forecast was $55,000.

For the data below: (a)Develop a scatter diagram. (b)Develop a five-year moving average forecast. (c)Find MAPE.

Deseasonalized data can be modeled as a straight line.

Time series models extrapolate historical data from the variable of interest.

Calculate (a)MAD, (b)MSE, and (c)MAPE for the following forecast versus actual sales figures.(Please round to four decimal places for MAPE.)

Describe the naïve forecasting method.

Demand for Y is shown in the table. Develop a forecast using a trend line.What is the forecast for period 12?