Forecasting functional time series using weighted likelihood methodology

Ufuk Beyaztas*, Han Lin Shang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)


Functional time series whose sample elements are recorded sequentially over time are frequently encountered with increasing technology. Recent studies have shown that analyzing and forecasting of functional time series can be performed easily using functional principal component analysis and existing univariate/multivariate time series models. However, the forecasting performance of such functional time series models may be affected by the presence of outlying observations which are very common in many scientific fields. Outliers may distort the functional time series model structure, and thus, the underlying model may produce high forecast errors. We introduce a robust forecasting technique based on weighted likelihood methodology to obtain point and interval forecasts in functional time series in the presence of outliers. The finite sample performance of the proposed method is illustrated by Monte Carlo simulations and four real-data examples. Numerical results reveal that the proposed method exhibits superior performance compared with the existing method(s).
Original languageEnglish
Pages (from-to)3046-3060
Number of pages15
JournalJournal of Statistical Computation and Simulation
Issue number16
Publication statusPublished - 2 Nov 2019
Externally publishedYes


  • Bootstrap
  • functional principal components
  • functional time series
  • weighted likelihood


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