Forecasting6

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Forecasting Techniques

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Forecasting6

  1. 1. Overview of Methods
  2. 2. Quantitative Techniques <ul><li>Moving Average </li></ul><ul><li>Trend Analysis </li></ul><ul><li>Exponential Smoothing </li></ul><ul><li>ARIMA models </li></ul><ul><li>Econometric models </li></ul>
  3. 3. Moving Average <ul><li>A simple average of the previous X months/years </li></ul><ul><li>A six-month moving average forecast is an average of the previous six months </li></ul>
  4. 4. “I always avoid prophesying beforehand because it much better to prophesy after the event has already taken place.” <ul><li>Winston Churchill </li></ul>
  5. 5. Moving Average – When To Use <ul><li>Extremely “noisy” or little data </li></ul><ul><li>Time constraint </li></ul><ul><li>Degree of accuracy not important </li></ul>
  6. 6. Moving Average - Advantages <ul><li>Extremely simple </li></ul><ul><li>Easy to implement </li></ul>
  7. 7. Moving Average - Disadvantages <ul><li>Not accurate; slow adjustment to changes in data </li></ul><ul><li>Misses turning points </li></ul><ul><li>All history is created equal </li></ul>
  8. 8. Moving Average Example
  9. 9. Trend Regression <ul><li>A straight ( or curved) line drawn through historical data </li></ul><ul><li>“taking a ruler through your data” </li></ul>
  10. 10. “The best qualification of a prophet is to have a good memory.” <ul><li>Marquis of Halifax </li></ul>
  11. 11. Trend Regression – When To Use <ul><li>Steady rise or decline in data </li></ul><ul><li>Time or software constraint </li></ul><ul><li>Need easy explanation </li></ul><ul><li>Little data </li></ul>
  12. 12. Trend Regression - Advantages <ul><li>Very simple </li></ul><ul><li>Can be done in Excel </li></ul>
  13. 13. Trend Regression – Disadvantages <ul><li>Assumes future is exactly like past (prices, economy, etc.) </li></ul><ul><li>All history is created equal </li></ul><ul><li>One bad data point can greatly affect forecast </li></ul>
  14. 14. Trend Regression Example
  15. 15. Exponential Smoothing <ul><li>Simple </li></ul><ul><li>Double (Brown) or Holt </li></ul><ul><li>Winters </li></ul>
  16. 16. “A good forecaster is not smarter than everyone else, he merely has his ignorance better organized.” <ul><li>C. W. J. Granger </li></ul>
  17. 17. Simple Exponential Smoothing <ul><li>Weighted average of past values with exponentially decreasing weights </li></ul><ul><li>Forecast this month equals last month’s forecast plus a proportion of the forecast error last month </li></ul>
  18. 18. Simple Exponential Smoothing – When To Use <ul><li>Stationary data with no trend or seasonality </li></ul>
  19. 19. Double (Brown) or Holt Exponential Smoothing <ul><li>Smooth the smoothed data with a weighted average of past values with exponentially decreasing weights </li></ul><ul><li>Changes linearly with time (like linear regression) with recent data given more weight </li></ul>
  20. 20. Double (Brown) or Holt Exponential Smoothing – When to Use <ul><li>Data with a trend but no seasonality </li></ul>
  21. 21. Winter’s Exponential Smoothing <ul><li>Deseasonalize data, then find trend, then smooth </li></ul>
  22. 22. Winter’s Exponential Smoothing – When to Use <ul><li>Data with trend and seasonality </li></ul>
  23. 23. Exponential Smoothing Advantages <ul><li>Somewhat simple </li></ul><ul><li>Recent data given more weight </li></ul><ul><li>Fairly good accuracy for short-term forecasts </li></ul><ul><li>Software can automate process </li></ul>
  24. 24. Exponential Smoothing - Disadvantages <ul><li>Requires forecasting software </li></ul><ul><li>Bad data in recent month can cause great error in forecast </li></ul><ul><li>Less accurate for medium to long-term forecasts </li></ul><ul><li>Assumes history is like (recent) history </li></ul>
  25. 25. Exponential Smoothing Example
  26. 26. ARIMA (Box-Jenkins) Models <ul><li>A uto R egressive I ntegrative M oving A verage </li></ul><ul><li>Autoregressive – future values depend on previous values of the data </li></ul><ul><li>Moving average – future values depend on previous values of the errors </li></ul><ul><li>Integrated – refers to differencing the data </li></ul>
  27. 27. “An unsophisticated forecaster uses statistics as a drunken man uses lamp-posts – for support rather than illumination” <ul><li>- after Andrew Lang </li></ul>
  28. 28. ARIMA (Box-Jenkins) Models – When to Use <ul><li>Stable data that has regular correlations </li></ul>
  29. 29. ARIMA ( Box-Jenkins) Models - Advantages <ul><li>Outperforms exponential smoothing on homogenous and stable data </li></ul><ul><li>Software can automate </li></ul><ul><li>Sounds impressive </li></ul>
  30. 30. ARIMA (Box-Jenkins) Models - Disadvantages <ul><li>Requires software </li></ul><ul><li>Needs a minimum of 40 data points </li></ul><ul><li>Complicated to understand </li></ul>
  31. 31. ARIMA (Box-Jenkins) Models Example
  32. 32. Econometric Models <ul><li>Relates data series to explanatory variables </li></ul><ul><li>Economists build demand models which relate </li></ul><ul><ul><li>Price, competition, income, population, etc. </li></ul></ul>
  33. 33. “An economist is an expert who will know tomorrow why the things he predicted yesterday didn’t happen today.” <ul><li>Evan Esar </li></ul>
  34. 34. Econometric Models – When to Use <ul><li>Important to understand market </li></ul><ul><li>Influences on product demand are changing </li></ul><ul><li>Historically more acceptable in regulation </li></ul>
  35. 35. Econometric Models - Advantages <ul><li>Can give price elasticity </li></ul><ul><li>Formally integrates economic impact </li></ul><ul><li>Permits varied assumptions, i.e., “what if?” </li></ul><ul><li>Forces you to make assumptions explicit </li></ul><ul><li>Methods to deal with short time </li></ul>
  36. 36. Econometric Models - Disadvantages <ul><li>Large data gathering </li></ul><ul><li>Expertise to build </li></ul><ul><li>Requires forecasts of explanatory variables </li></ul><ul><li>Not always best forecasting technique </li></ul>
  37. 37. Econometric Models Example
  38. 38. Res. DA Model <ul><li>#DA calls per person = 4.49-0.18*Price +1.04*Income per person+0.00016*Timetrend </li></ul>
  39. 39. Summary <ul><li>Graph data </li></ul><ul><li>Choose appropriate technique for </li></ul><ul><ul><li>Output </li></ul></ul><ul><ul><li>Time </li></ul></ul><ul><ul><li>Data </li></ul></ul><ul><li>Know advantages and disadvantages </li></ul>

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