Time, or temporal, concerns about demand levels are common in forecasting. Demand variation with time is a result of growth or decline in sales rates, seasonality in the demand pattern, and general fluctuations caused by a multitude of factors. Most short-term forecasting methods deal with this type of temporal variation, often referred to as a time series.

Logistics has both space and time dimensions. That is, the logistician must know where demand volume will take place as well as when it will take place. Spatial location of demand is needed to plan warehouse locations, balance inventory levels across the logistics network, and geographically allocate transportation resources. Forecasting techniques should be selected to reflect geographic differences that may affect demand patterns. Also, the techniques may differ depending on whether all demand is forecasted and then disaggregated by geographic location (top-down forecasting) or whether each geographic location is forecasted separately and aggregated if necessary (bottom-up forecasting).