1. ADJUSTED EXPONENTIAL SMOOTHING FORECASTING METHOD Prepared by Dan Milewski November 29, 2005

2. 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

3. Defining the Method A Forecasting Model: Predicts future levels of a variable Can be either quantitative or qualitative 1234567

4. Defining the Method Exponential Smoothing: Quantitative forecasting method Weighted average of two variables 1234567

5. Defining the Method Adjusted Trend adjustment factor included Better at picking up on trends 1234567

6. 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. 1234567

7. 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 1234567

8. When to Use the Method 1234567

9. Manufacturing Firms: –To forecast demand Service Organizations: –To forecast customer arrival patterns Financial Analysts: –To forecast revenues and profits Investors: –To forecast economic indicators 1234567

10. How to Use the Method Exponential Smoothing: F t+1 =  D t + (1 - )F t Where… F t +1 =forecast for next period F t +1 =forecast for next period D t =actual value for present period D t =actual value for present period F t =previously determined forecast for present period F t =previously determined forecast for present period  =weighting factor (between 0 and 1)  =weighting factor (between 0 and 1) 1234567

11. How to Use the Method Adjusted Exponential Smoothing: AF t+1 = F t+1 + T t+1 Where… T t +1 = (F t+1 – F t ) + (1 - ) T t T t +1 = (F t+1 – F t ) + (1 - ) T t = trend factor for the next period = trend factor for the next period T t = trend factor for the current period T t = trend factor for the current period = smoothing constant for the trend = smoothing constant for the trend adjustment factor adjustment factor (just add a trend adjustment factor) 1234567

12. How to Use the Method 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 1234567

13. An Example 2005 U.S. Housing Starts (monthly): 1234567

14. An Example 2005 U.S. Housing Starts (monthly): 1234567

15. 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 1234567

16. An Exercise Intel Quarterly Sales Revenue 1234567

17. An Exercise Intel Quarterly Net Income 1234567

18. 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? 1234567

19. Summary Adjusted Exponential Smoothing Forecasting Method: Quantitative forecasting model Highly accurate Best when trends exist 1234567

20. 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 1234567