Forecasting Applications of an Adaptive Multiple Exponential Smoothing Model
提出一类多重指数平滑模型，适用于自动化或最少干预的工业预测系统。该模型能处理多个时间序列间的相互关系，并自适应调整平滑矩阵，通过最大似然估计参数，避免人为设定。用汽车销售数据验证，效果优于单变量模型。
This paper introduces a class of multiple exponential smoothing models useful in automated or minimal intervention industrial forecasting systems. These models are an alternative to simple univariate exponential smoothing and Trigg and Leach type adaptive models, which treat time series as unrelated and so cannot explicitly accommodate interrelationships that may exist between two or more time series. Moreover, the multiple models are adaptive in that the smoothing matrix, which is a generalization of the smoothing constant of univariate models, changes from period to period. Maximum likelihood estimates of the model parameters, including the full variance-covariance structure as well as the smoothing matrix, are provided, thus freeing the model user from the need for making ad hoc estimates of parameter values, a feature of simple univariate exponential smoothing. The forecast performance of this multiple time series model is compared with that of other univariate models using automobile sales data and some promising results are obtained.