AN ADAPTIVE MULTIVARIATE APPROACH TO TIME SERIES FORECASTING

Stuart Bretschneider, Robert Carbone, Richard L. Longini

Research output: Contribution to journalArticle

4 Scopus citations

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. 1982 by the American Institute for Decision Sciences

Original languageEnglish (US)
Pages (from-to)668-680
Number of pages13
JournalDecision Sciences
Volume13
Issue number4
DOIs
StatePublished - 1982
Externally publishedYes

ASJC Scopus subject areas

  • Business, Management and Accounting(all)
  • Strategy and Management
  • Information Systems and Management
  • Management of Technology and Innovation

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