Multivariate exponential smoothing: Method and practice

D. Pfeffermann*, J. Allon

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

27 Scopus citations

Abstract

A multivariate extension of the familiar exponential smoothing procedure for forecasting univariate time series composed of level, seasonality and irregularity is presented. The updated estimates of the level, trend and seasonal effects of each of the component series are obtained as weighted averages of estimates derived by a univariate smoothing procedure and correction factors based on information gained from the other series. The forecasts are shown to be asymptotically optimal under particular state space models. The performance of the procedure is illustrated and compared to that of other univariate and multivariate forecasting procedures using two actual bivariate time series. The results are encouraging for the use of the procedure in practice.

Original languageEnglish
Pages (from-to)83-98
Number of pages16
JournalInternational Journal of Forecasting
Volume5
Issue number1
DOIs
StatePublished - 1989

Keywords

  • ARIMA models
  • Kalman filter
  • Prediction accuracy
  • Seasonality
  • Steady state
  • Structural model
  • Trends

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