Introducing C.A.R.T. To The Forecasting Process

INTRODUCING C.A.R.T. TO THE FORECASTING PROCESS By Jeff Morrison CART makes it easier to have an in depth insight into different segments of data ...has advantages over regression in handling missing data and capturing nonlinearity and interactions within the data ... currently used quite successfully in the credit card industry for prescreening credit card mailings. The econometric literature on segmentation has historically shown that regression based forecasting models work best when applied to homogeneous groups. This makes sense for a number of reasons. Factors affecting one population may not be the same factors that impact others. In forecasting telephone lines, for example, variables describing more complex dynamics in pricing, technology, and marketing may better describe the demand for business lines than residential connections. Even within the variables themselves there may exist certain groupings that may behave differently from one another. For example, may be the propensity to purchase consumer durables is different between high income and low income groups. Perhaps customers residing in various geographic locations behave very similarly. Although the forecaster has ...