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Deseasonalizing Forecasts

Deseasonalizing Forecasts. Prepared by Aaron Hirst Brigham Young University Fall 2005. Agenda:. Seasonality defined & seasonal adjustment methods Brainstorming Exercise Nuts and Bolts How It Works Seasonal adjustment example Exercise Summary Reading List

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Deseasonalizing Forecasts

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  1. Deseasonalizing Forecasts Prepared by Aaron Hirst Brigham Young University Fall 2005

  2. Agenda: • Seasonality defined & seasonal adjustment methods • Brainstorming Exercise • Nuts and Bolts • How It Works • Seasonal adjustment example • Exercise • Summary • Reading List • Appendix A: Solution to Exercise

  3. Seasonality • A repeated pattern of spikes or drops in the variable of interest associated with a period of time • Examples- • Consumer buying habits • Price of gasoline

  4. Seasonality • Causes of seasonal movement by class: 1. Weather (temperature, precipitation) 2. Calendar Events (religious or secular festivals) 3. Timing decisions (vacations, accounting periods)

  5. Seasonal Adjustment Methods • X-12 ARIMA • X-11 ARIMA • EEC Method • Burman Method • TRAMO • Seasonal index

  6. Brainstorming Exercise • How can this tool be used in your organization?

  7. Nuts and Bolts Why make seasonal adjustments? • Reduces errors in time-series forecasting • Improves quality of judgmental forecasts • Gives good insight into the factors influencing demand The purpose of finding seasonal indexes is to remove the seasonal effects from the time series

  8. How It Works: Deseasonalizing Forecasts Four-step procedure for seasonal adjustments: 1. Calculate forecast for each demand values in the time series 2. For each demand value, calculate Demand/Forecast 3. Average the Demand/Forecast for months or quarters to get the seasonal index 4. Multiply the unadjusted forecast by the seasonal index to find adjusted forecast value

  9. Season Adjustment Example • Foster Company makes widgets. The quarterly demand for its widget is given in Exhibit 1 • Using linear regression forecasting, develop a seasonal index for each quarter and reforecast each quarter

  10. Seasonal Adjustments ExampleStep 1 Calculate forecast for each demand values in the time series • Use the unadjusted regression forecast model Y= a + bx

  11. Seasonal Adjustments Example – Step 1 • Forecasted demand Y=95.85+4.03*period • Year 1 Quarter 1: Y=95.85+4.03(1) =99.9

  12. Seasonal Adjustments Example – Step 2 • For each demand value, calculate Demand/Forecast • Year 1 Quarter 1: 72/99.9= 0.72

  13. Seasonal Adjustments Example - Step 3 • Average the Demand/Forecast for the quarters to get the seasonal index • Quarterly Seasonal Index for Quarter 1: (0.72+0.66+0.59+0.55)/4 = 0.63

  14. Seasonal Adjustments Example - Step 4 • Multiply the unadjusted forecast by the seasonal index to find the adjusted forecast values • Year 1 Quarter 1: 99.9 * 0.63 = 62.7 (adjusted forecast)

  15. Seasonal Adjustments Example - Step 4

  16. Seasonal Adjustments Example

  17. Exercise • Smith Company makes widgets. The quarterly demand for its widget is given in Exhibit A • You have been asked to develop a seasonal index for each quarter and reforecast each quarter

  18. Exercise Table Click here to check your answer

  19. Summary • Deseasonalizing forecasts is effective for • Short-term forecasting • Comparability • Detecting trend changes early • The Seasonal Index is the most simple method for making seasonal adjustments

  20. Reading List • Armstrong, J. Scott. Principles of Forecasting: A Handbook for Researchers and Practitioners. Norwell: Kluwer Academic Publishers, 2001. • Bozarth, Cecil C. and Robert B. Handfield. Introduction To Operations And Supply Chain Management. New Jersey: Prentice Hall, 2006. • DeLurgio, Stephen and Carl Bhame. Forecasting Systems For Operations Management. Homewood: Irwin, 1991. • Hylleberg, Svend. Modeling Seasonality. New York: Oxford Press, 1992. • Hylleberg, Svend. Seasonality in Regression. Orlando: Academic Press Inc., 1986.

  21. Appendix A: Solution to Exercise

  22. Appendix A: Solution to Exercise

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