ADJUSTED EXPONENTIAL SMOOTHING
FORECASTING METHOD
Prepared by Dan Milewski
November 29, 2005
Tutorial Outline
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Defining the Method
When to Use the Method
How to Use the Method
An Example
An Exercise
Summary
Readings List
Defining the Method
A Forecasting Model:
• Predicts future levels of a variable
• Can be either quantitative or qualitative
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Defining the Method
Exponential Smoothing:
• Quantitative forecasting method
• Weighted average of two variables
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Defining the Method
Adjusted
• Trend adjustment factor included
• Better at picking up on trends
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Defining the Method
So, combined,….
Adjusted Exponential Smoothing Forecasting Method:
A method that uses measurable, historical data
observations, to make forecasts by calculating the
weighted average of the current period’s actual value
and forecast, with a trend adjustment added in.
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When to Use the Method
• Preferred Scenario:
– When a trend is present
• Good Scenario:
– When there’s a cyclical or seasonal pattern
• Least-effective Scenario
– Working with random variations
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When to Use the Method
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When to Use the Method
• Manufacturing Firms:
– To forecast demand
• Service Organizations:
– To forecast customer arrival patterns
• Financial Analysts:
– To forecast revenues and profits
• Investors:
– To forecast economic indicators
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How to Use the Method
Exponential Smoothing:
Ft+1 =
Where…
Ft +1 =
Dt =
Ft =
Dt + (1 -
)Ft
forecast for next period
actual value for present period
previously determined forecast for
present period
= weighting factor (between 0 and 1)
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How to Use the Method
Adjusted Exponential Smoothing:
AFt+1 = Ft+1 + Tt+1
Where…
Tt +1 =
(Ft+1 – Ft ) + (1 - ) Tt
= trend factor for the next period
Tt = trend factor for the current period
= smoothing constant for the trend
adjustment factor
(just add a trend adjustment factor)
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How to Use the Method
Points to Consider:
• To start, pick an unadjusted forecast
• In period 1, trend equals 0
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An Example
2005 U.S. Housing Starts (monthly):
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An Example
2005 U.S. Housing Starts (monthly):
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An Exercise
Using the adjusted exponential smoothing forecasting
method and the following data…
– Predict Q4 2005 sales revenues for Intel
• Where = 0.4 and = 0.7
– Predict Q4 2005 net income for Intel
• Where = 0.2 and = 0.6
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An Exercise
Intel Quarterly Sales Revenue
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An Exercise
Intel Quarterly Net Income
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An Exercise
• Which series of data best fits with this method?
• What makes this so?
• What other financial data could be predicted
accurately with this method?
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Summary
Adjusted Exponential Smoothing Forecasting Method:
• Quantitative forecasting model
• Highly accurate
• Best when trends exist
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Readings List
• Gardner, Jr., E.S. Exponential Smoothing: The
State of the Art. Journal of Forecasting. April 1985,
Vol. 3, Iss. 1.
• Jain, Chaman L. Business Forecasting Practices in
2003. The Journal of Business Forecasting
Methods & Systems. Fall 2004, Vol. 23, Iss. 3
• http://home.ubalt.edu/ntsbarsh/ECON/lecture6.doc
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