Forecasting long memory series subject to structural change: A two-stage approach

Fotis Papailias, Gustavo Fruet Dias

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)
14 Downloads (Pure)


A two-stage forecasting approach for long memory time series is introduced. In the first step, we estimate the fractional exponent and, by applying the fractional differencing operator, obtain the underlying weakly dependent series. In the second step, we produce multi-step-ahead forecasts for the weakly dependent series and obtain their long memory counterparts by applying the fractional cumulation operator. The methodology applies to both stationary and nonstationary cases. Simulations and an application to seven time series provide evidence that the new methodology is more robust to structural change and yields good forecasting results.
Original languageEnglish
Pages (from-to)1056-1066
Number of pages11
JournalInternational Journal of Forecasting
Issue number4
Early online date3 Aug 2015
Publication statusPublished - 1 Oct 2015


  • Time series forecasting
  • Spurious long memory
  • Fractional integration
  • Local Whittle

Cite this