Functional time series prediction under partial observation of the future curve
Type
ArticleKAUST Department
Biostatistics GroupComputer, Electrical and Mathematical Science and Engineering (CEMSE) Division
Statistics Program
Date
2021-05-17Preprint Posting Date
2019-06-01Embargo End Date
2022-05-11Permanent link to this record
http://hdl.handle.net/10754/669155
Metadata
Show full item recordAbstract
This paper tackles one of the most fundamental goals in functional time series analysis which is to provide reliable predictions for functions. Existing functional time series methods seek to predict a complete future functional observation based on a set of observed complete trajectories. The problem of interest discussed here is how to advance prediction methodology to cases where partial information on the next trajectory is available, with the aim of improving prediction accuracy. To solve this problem, we propose a new method "partial functional prediction (PFP)". The proposed method combines "next-interval" prediction and fully functional regression prediction, so that the partially observed part of the trajectory can aid in producing a better prediction for the unobserved part of the future curve. In PFP, we include automatic selection criterion for tuning parameters based on minimizing the prediction error. Simulations indicate that the proposed method can outperform existing methods with respect to mean-square prediction error and its practical utility is illustrated in an analysis of environmental and traffic flow data.Citation
Jiao, S., Aue, A., & Ombao, H. (2021). Functional time series prediction under partial observation of the future curve. Journal of the American Statistical Association, 1–29. doi:10.1080/01621459.2021.1929248arXiv
1906.00281Additional Links
https://arxiv.org/pdf/1906.00281.pdfae974a485f413a2113503eed53cd6c53
10.1080/01621459.2021.1929248