adjusted exponential smoothing
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DJUSTED EXPONENTI L SMOOTHING
FOREC STING METHOD
Prepared by Dan Milewski
November 29, 2005
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Tutorial Outline
1. Defining the Method
2. When to Use the Method
3. How to Use the Method
4. An Example
5. An Exercise
6. Summary
7. Readings List
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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 theweighted averageof the current periods actual value
andforecast, with a trend adjustmentadded in.
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When to Use the Method
Preferred Scenario:
When a trendis present
Good Scenario:
When theres a cyclicalorseasonalpattern Least-effective Scenario
Working with randomvariations
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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= Dt+ (1 - )Ft
Where
Ft +1 = forecast for next period
Dt = actual value for present periodFt = 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+1Ft )+ (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 ForecastingMethods & Systems. Fall 2004, Vol. 23, Iss. 3
http://home.ubalt.edu/ntsbarsh/ECON/lecture6.doc
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