AN ADAPTIVE MULTIVARIATE APPROACH TO TIME SERIES FORECASTING
提出一种简化的多元时间序列预测方法，基于Carbone-Longini自适应估计过程，无需先验信息即可适应系统变化，通过两个经典数据集验证了其在提高预测精度和简化分析方面的优势。
ABSTRACT In recent years, time series analysts have shifted their interest from univariate to multivariate forecasting approaches. Among them, the Box‐Jenkins transfer function process and the state space method have received the most attention. This paper presents a simplified approach that embodies some desirable features of existing methods. It stresses empirical analysis, has a unified modeling structure, is easily applicable, and is adaptive to changes without necessitating prior information on the evolution of a system under study. The core of the method relies on the Carbone‐Longini adaptive estimation procedure (AEP). Results of a comparative study based on the well‐known Lydia E. Pinkham data and the Box‐Jenkins sales/leading indicator data illustrate the merits of multivariate AEP in improving forecasting accuracy while simplifying the analysis process. Subject Area: Forecasting .