Situational Factors Affecting Forecast Accuracy
比较五种外推法预测与随机游走模型在近1500种情境下的表现，发现非耐用品和历史稳定的序列中外推法更优，而数据量和产品分类对模型选择不重要。
Forecasts generated by five popular extrapolations are compared with each other and with a random-walk model over nearly 1500 situations. Relative to the random walk, forecasting with extrapolations is most successful on nondurable goods and series that historically have exhibited stable patterns. The amount of data available for forecasting and the product class/product form typology are not found to be important factors in the selection of an extrapolation model.