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AN ADAPTIVE MULTIVARIATE APPROACH TO TIME SERIES FORECASTING
Author(s) -
Bretschneider Stuart I.,
Carbone Robert,
Longini Richard L.
Publication year - 1982
Publication title -
decision sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.238
H-Index - 108
eISSN - 1540-5915
pISSN - 0011-7315
DOI - 10.1111/j.1540-5915.1982.tb01898.x
Subject(s) - univariate , multivariate statistics , computer science , series (stratigraphy) , process (computing) , time series , data mining , econometrics , machine learning , mathematics , paleontology , biology , operating system
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 .