Count Data and Intermittent Demands
So far, we have focused on using the normal distribution for forecasting. Using the normal distribution can be inappropriate for two reasons: First, the normal distribution is continuous, that is, a normally distributed random variable can take noninteger values, like 2.43. Second, the normal distribution is unbounded, that is, a normally distributed random variable can take negative as well as positive values. Both these properties of the normal distribution do not make sense for (most) demand time series. Demand is usually integer-valued, apart from products sold by weight or volume, and demand is usually zero or positive, but not negative, apart from returns.
These seem pretty ...
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