QMB 3250 FINAL EXAM PREP QUESTIONS WITH
100% CORRECT SOLUTIONS!!
Seasonal Component
Fluctuation is the same magnitude at the same time each year
deseasonalized
seasonal component removed
period
time between 2 peaks
smoothing methods advantages and disadvantages
Advantage: No assumptions must be made about a trend or about a seasonal component.
Disadvantage: Predictions are limited - can only predict in the very short term. These are usually
only used for one period beyond the data recorded.
the smooth point before is the
predicted point at the next time
Disadvantages of moving averages
The moving average values can be affected by outliers (sharp peaks or falls).
Doesn't work well when there are strong seasonal, trend or cyclical components.
Smaller alpha in simple exponential smoothing
smoother
, alpha close to 0.5
the most recent value and historic values are weighted the same
alpha is close to 1
the most recent value is given more weight
You want to use this if you want the series to react more quickly to irregular components.
alpha close to 0
the most recent value is given less weight than historic values.
You want to use this if you want the series to react more slowly, creating a more stable series
disadvantages of simple exponential smoothing
Doesn't usually work well when seasonal components are present
Also misses peaks
simple exponential smoothing forecast error
real - predicted
autoregressive model
uses regression methods to determine the weights for previous terms
best for quarterly trends
100% CORRECT SOLUTIONS!!
Seasonal Component
Fluctuation is the same magnitude at the same time each year
deseasonalized
seasonal component removed
period
time between 2 peaks
smoothing methods advantages and disadvantages
Advantage: No assumptions must be made about a trend or about a seasonal component.
Disadvantage: Predictions are limited - can only predict in the very short term. These are usually
only used for one period beyond the data recorded.
the smooth point before is the
predicted point at the next time
Disadvantages of moving averages
The moving average values can be affected by outliers (sharp peaks or falls).
Doesn't work well when there are strong seasonal, trend or cyclical components.
Smaller alpha in simple exponential smoothing
smoother
, alpha close to 0.5
the most recent value and historic values are weighted the same
alpha is close to 1
the most recent value is given more weight
You want to use this if you want the series to react more quickly to irregular components.
alpha close to 0
the most recent value is given less weight than historic values.
You want to use this if you want the series to react more slowly, creating a more stable series
disadvantages of simple exponential smoothing
Doesn't usually work well when seasonal components are present
Also misses peaks
simple exponential smoothing forecast error
real - predicted
autoregressive model
uses regression methods to determine the weights for previous terms
best for quarterly trends
