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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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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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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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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adjusted exponential smoothing

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