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# Of week 1 2 3 4 5 6 fp1 92 87 95 90 88 93 adp1 88 88

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Unformatted text preview: turer. of Week 1 2 3 4 5 6 FP1 92 87 95 90 88 93 ADP1 88 88 97 83 91 93 |e1| FP2 96 89 92 93 90 85 ADP2 91 89 90 90 86 89 |e2| Biases in Forecasts A bias occurs when the average value of a forecast error average value tends to be positive or negative. Mathematically an unbiased forecast is one in which E(e i ) = 0. E(e 7 Forecasting for Stationary Series A stationary time series has the form: stationary Dt = µ + εt where µ iis a constant and εt iis a random s where s variable with mean zero and variance σ2. Two common methods for forecasting stationary series are moving averages and exponential smoothing. Moving Averages (MA) Moving A moving average of order N iis simply the arithmetic s average of the most recent N observations. For a oneobservations. step-ahead forecast: ahead Ft = (1/N) (Dt - 1 + Dt - 2 + . . . + Dt - n ) 8 Example Week 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 Actual Demand 100 125 90 110 105 130 85 102 110 90 105 95 115 120 80 95 100 3week MA Forecast error 5week MA Forecast error 7week MA Forecast error Moving Averages Advantages of Moving Average Method Advantages Easily understood Easily Easily computed Easily Provides stable forecasts Provides Disadvantages of Moving Average Method Disadvantages Requires saving all past N data points Requires Lags behind a trend Lags Ignores complex relationships in data Ignores 9 Moving Average Lags a Trend Moving Example Example Consider a case where observer demand is 2,4,6,8,10,12,14,16,18,22,24. So...
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