Functional Time Series Prediction Under Partial Observation of the Future Curve

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Author(s)

Detail(s)

Original languageEnglish
Pages (from-to)315-326
Number of pages12
Journal / PublicationJournal of the American Statistical Association
Volume118
Issue number541
Online published17 May 2021
Publication statusPublished - 2023
Externally publishedYes

Abstract

This article tackles one of the most fundamental goals in functional time series analysis which is to provide reliable predictions for future functions. Existing methods for predicting a complete future functional observation use only completely observed trajectories. We develop a new method, called partial functional prediction (PFP), which uses both completely observed trajectories and partial information (available partial data) on the trajectory to be predicted. The PFP method includes an automatic selection criterion for tuning parameters based on minimizing the prediction error, and the convergence rate of the PFP prediction is established. Simulation studies demonstrate that incorporating partially observed trajectory in the prediction outperforms existing methods with respect to mean squared prediction error. The PFP method is illustrated to be superior in the analysis of environmental data and traffic flow data. © 2021 American Statistical Association.

Research Area(s)

  • Dimension reduction, Functional principal component, Final prediction error, Functional time series, Intra-day fully functional linear regression model, Long-term and short-term dynamics, Updating prediction

Citation Format(s)

Functional Time Series Prediction Under Partial Observation of the Future Curve. / Jiao, Shuhao; Aue, Alexander ; Ombao, Hernando.
In: Journal of the American Statistical Association, Vol. 118, No. 541, 2023, p. 315-326.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review