Question

Differentiate between causal decision models and time series decision models, explaining how differently they work and what kinds of decisions or predictions they are each meant to address.

Answer 1

Time decision model focuses entirely on patterns and pattern changes, and thus relies entirely on historical data.

The casual decision models use highly refined and specific information about relationships between system elements, and is powerful enough to take special events formally into account. As with time series analysis and projection techniques, the past is important to causal models.

These differences imply (quite correctly) that the same type of forecasting technique is not appropriate to forecast sales, say, at all stages of the life cycle of a product—for example, a technique that relies on historical data would not be useful in forecasting the future of a totally new product that has no history.