Updating ARMA predictions for temporal aggregates

Sergio G. Koreisha (First Author), Yue Fang (Participant Author)

Research output: Contribution to journalJournal

8 Citations (Web of Science)


This article develops and extends previous investigations on the temporal aggregation of ARMA predications. Given a basic ARMA model for disaggregated data, two sets of predictors may be constructed for future temporal aggregates: predictions based on models utilizing aggregated data or on models constructed from disaggregated data for which forecasts are updated as soon as the new information becomes available. We show that considerable gains in efficiency based on mean-square-error-type criteria can be obtained for short-term predications when using models based on updated disaggregated data. However, as the prediction horizon increases, the gain in using updated disaggregated data diminishes substantially. In addition to theoretical results associated with forecast efficiency of ARMA models, we also illustrate our findings with two well-known time series.
Original languageEnglish
Pages (from-to)275-296
JournalJournal of Forecasting
Issue number4
Publication statusPublished - 2004

Corresponding author email



  • ARMA processes
  • nonstationary processes
  • prediction
  • seasonal ARMA processes
  • temporal aggregation

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  • Scopus
  • SSCI


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