Causal methods use data from sources other than the series being predicted.
If Y is the phenomenon to be forecast and X 1 , X 2 , . .., X n are the n variables we believe to be related to Y, then a causal model is one in which the forecast for Y is some function of these variables:
Y = f (X 1 , X 2 , . .., X n )
Econometric models are causal models in which the relationship between Y and (X 1 , X 2 , . .., X n ) is linear.
Current forecast is a weighted average of the last forecast and the current value of demand
New forecast = α (current observation of demand)
+ (1- α ) (last forecast)
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Exponential Smoothing F t = F t-1 – (fraction of the observed forecast error in t-1) If we forecast high in period t-1 error is positive adjustment to decrease current forecast If we forecast low in period t-1 error is negative adjustment to increase current forecast
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Exponential Smoothing for Different Values of Alpha Fig. 2-6
38.
Smaller values of α produce more stable forecasts, whereas larger values of α will produce forecasts which react more quickly to changes in the demand pattern.