Technological Forecasting—Model Selection, Model Stability, and Combining Models
识别了29个技术预测模型，按拐点时机分为三类，通过拟合度和稳定性选择模型，发现识别模型类别比选最优模型更容易，组合预测表现优于单个模型。
The paper identifies 29 models that the literature suggests are appropriate for technological forecasting. These models are divided into three classes according to the timing of the point of inflexion in the innovation or substitution process. Faced with a given data set and such a choice, the issue of model selection needs to be addressed. Evidence used to aid model selection is drawn from measures of model fit and model stability. An analysis of the forecasting performance of these models using simulated data sets shows that it is easier to identify a class of possible models rather than the ‘best’ model. This leads to the combining of model forecasts. The performance of the combined forecasts appears promising with a tendency to outperform the individual component models.