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    Functional time series prediction under partial observation of the future curve

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    Jiao-JASA.pdf
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    Description:
    Accepted Manuscript
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    Type
    Article
    Authors
    Jiao, Shuhao
    Aue, Alexander
    Ombao, Hernando cc
    KAUST Department
    Biostatistics Group
    Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
    Statistics Program
    Date
    2021-05-17
    Preprint Posting Date
    2019-06-01
    Embargo End Date
    2022-05-11
    Permanent link to this record
    http://hdl.handle.net/10754/669155
    
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    Abstract
    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:10.1080/01621459.2021.1929248
    Publisher
    Accepted by American Statistical Association
    Journal
    Journal of the American Statistical Association
    DOI
    10.1080/01621459.2021.1929248
    arXiv
    1906.00281
    Additional Links
    https://arxiv.org/pdf/1906.00281.pdf
    ae974a485f413a2113503eed53cd6c53
    10.1080/01621459.2021.1929248
    Scopus Count
    Collections
    Articles; Statistics Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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