12_posting_-_Forecasting_2 - Forecasting 2 Trying to...

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10/15/2010 1 Forecasting 2 Trying to predict the future is like trying to drive down a country Trying to predict the future is like trying to drive down a country road at night with no lights while looking out the back window road at night with no lights while looking out the back window . Peter Drucker
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10/15/2010 2 How can we compare across forecasting models? We need a metric that provides estimation of accuracy Forecast Error Forecast error = Difference between actual and forecasted value (also known as residual ) Errors can be: 1. biased (consistent) 2. random 3. combination
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10/15/2010 3 Measuring Accuracy: MAD MAD = Mean Absolute Deviation It is the average absolute error in the observations 1. Higher MAD implies worse performance (higher deviation from forecast – VARIATION ). 2. If errors are normally distributed, then σ ε =1.25MAD (or 1 MAD = 0.8 σ ε )
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10/15/2010 4 Measuring Accuracy: MAD 99.73% of all actual demand points should fall within + 3.75 MAD from forecast demand
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10/15/2010 5 Measuring Accuracy: RSFE RSFE = Running Sum of Forecast Errors It is the sum of all errors (not absolute) in the observations Again, higher RSFE indicates worse performance (more deviation from forecast – BIAS )
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10/15/2010 6 Measuring Accuracy: Tracking signal The tracking signal is a measure of how often our predictions have been above or below the actual value and the magnitude of the error MAD RSFE TS = Positive tracking signal: the actual value is above our predicted value Negative tracking signal: the actual value is below our predicted value
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10/15/2010 7 Example: bottled water at Kroger Month Actual Forecast Jan 1,325 1370 Feb 1,353 1341 Mar 1,305 1322 Apr 1,275 1300 May 1,210
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