ADJUSTED EXPONENTIAL SMOOTHING FORECASTING METHOD

Prepared by Dan Milewski

November 29, 2005

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

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:Exponential Smoothing:

FFtt+1+1 = = DDtt + (1 - ) + (1 - )FFtt

Where…Where…

FFt t +1 +1 = = forecast for next periodforecast for next period

DDtt = = actual value for present periodactual value for present period

FFtt = = previously determined forecast for previously determined forecast for

present periodpresent period

== weighting factor (between 0 and 1)weighting factor (between 0 and 1)

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How to Use the Method

Adjusted Exponential Smoothing:Adjusted Exponential Smoothing:

AFAFtt+1+1 = = FFtt+1+1 + T + Ttt+1+1

Where…Where… TTt t +1 +1 = ( = (FFtt+1+1 – – FFt t )) + (1 - ) + (1 - ) TTtt

= trend factor for the next period= trend factor for the next period TTtt = trend factor for the current period = trend factor for the current period = smoothing constant for the trend = smoothing constant for the trend

adjustment factoradjustment factor

(just add a trend adjustment factor)(just add a trend adjustment factor)1 2 3 4 5 6 7

How to Use the Method

Points to Consider:Points to Consider:

• To start, pick an unadjusted forecastTo start, pick an unadjusted forecast

• In period 1, trend equals 0In period 1, trend equals 0

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An Example

2005 U.S. Housing Starts (monthly):2005 U.S. Housing Starts (monthly):

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An Example

2005 U.S. Housing Starts (monthly):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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