1. Section 7.2: Exponential Smoothing Quantitative Decision Making 7 th ed By Lapin and Whisler

2. Simple Exponential Smoothing YeartYtYt FtFt 200012014------- 2001216482014 2002316941904 2003421151841 2004521671923 2005624101996 2006724642120 2007821452223 2008912102200 200910270-------

3. Graphing Actual vs Forecast Values

4. Forecasting Errors

5. Two Parameter Smoothing

6. Simple Exponential Smoothing YeartYtYt TtTt btbt FtFt 200012014---------------------- 2001216482014 1648-2014 -366 ------- 200231694 2014-366 1648 200342115

7. Compute T 3 YeartYtYt TtTt btbt FtFt 200012014---------------------- 2001216482014-366------- 20023169416611648 200342115

8. Compute b 3 YeartYtYt TtTt btbt FtFt 200012014---------------------- 2001216482014-366------- 2002316941661-3631648 200342115

9. Seasonal Exponential Smoothing with Three Parameters  Many time series have regular seasonal patterns to be incorporated into forecasts.  The three-parameter model incorporates a seasonal smoothing constant  (beta): T t =  Y t /S t –p ) + (1 –  )(T t –1 + b t –1 ) b t =  (T t – T t –1 ) + (1 –  )b t –1 S t =  Y t /T t ) + (1 –  )S t –p F t+1 = (T t + b t ) S t –p+1

10. Forecasting with Three Parameters

11.  The above works for p = 4 quarters or p = 12 months.  The preceding slide needs 6 quarters to generate the first (very bad) forecast.  The process settles quickly, providing good forecasts p periods into the future.