Decision support systems often rely on time series forecasting, making the accuracy of such systems of paramount importance for their efficiency. Since most systems nowadays require the processing of massive amount of data, automatic identification of time series models has become inevitable. This automatism is inherent in artificial intelligence methods, but it often goes unnoticed that forecasting ‘classical’ methods have also been developing their own automatic methods for a long time. The radical novelty of this paper is the development of a brand new algorithm for identification of structural Unobserved Components models from which decision support systems may benefit. A second point is that combination of forecasts is more fruitful than competition or method selection in some cases. Both points are illustrated in two examples that show the effectiveness of the identification procedure and the forecasting gains when fairly different methods are combined.

决策支持系统通常依赖于时间序列预测，因此此类系统的准确性对其效率至关重要。由于当今大多数系统都需要处理海量数据，因此时间序列模型的自动识别已成为必然。这种自动化是人工智能方法所固有的，但人们常常忽视的是，预测“经典”方法也已经开发了自己的自动化方法很长时间了。本文的根本新颖之处在于开发了一种全新的算法，用于识别结构性未观察到的组件模型，决策支持系统可以从中受益。第二点是在某些情况下，预测的组合比竞争或方法选择更有成效。