Stats Ch 14 Time Series Forecasting- Scripted(2).pptx

Model i exponential smoothing w 2 model ii

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Model I: Exponential smoothing ( w = .2) Model II: Exponential smoothing ( w = .7) Model III: Linear regression with x=t
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22 Forecasting Accuracy Example Model I 2.31 4.66 6.01 9.14 5.53 4 I MAD 2.31 4.66 6.01 9.14 49.96 56.93 58.28 61.41 100 9.50 4 I MAPE 2 2 2 2 2.31 4.66 6.01 9.14 6.06 4 I RMSE
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23 Forecasting Accuracy Example Model II 2.82 4.15 5.50 8.63 5.28 4 II MAD 2.82 4.15 5.50 8.63 49.96 56.93 58.28 61.41 100 9.11 4 II MAPE 2 2 2 2 2.82 4.15 5.50 8.63 5.70 4 II RMSE
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24 Forecasting Accuracy Example Model III 3.45 2.42 2.67 4.71 3.31 4 III MAD 3.45 2.42 2.67 4.71 49.96 56.93 58.28 61.41 100 5.85 4 III MAPE 2 2 2 2 3.45 2.42 2.67 4.71 3.44 4 III RMSE
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25 (iv) Seasonal Regression Model Takes into account trend and seasonal effects (seasonal component) Uses multiple regression models Dummy variables to model seasonal component E( Y t ) = β 0 + β 1 t + β 2 Q 1 + β 3 Q 2 + β 4 Q 3 where = 1 if quarter 0 if not quarter i i Q i
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E( Y t ) = β 0 + β 1 t + β 2 Q 1 + β 3 Q 2 + β 4 Q 3 0 5 10 15 20 25 0 100 200 300 400 500 600 700 Y t=Time Period Sales Trend_Seasonal Data Sales with Coded data
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28 Seasonal Sales Coded
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29 The Q i provides the multiplier for the i th quarter relative to the 4 th quarter Q i = 1 if it is the i th quarter, and 0 if not t= the coded variable denoting the time period β 1 For every increase in time period, the mean sales increase by an estimated $16.51
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