Prediction theory for stationary functional time series
Author:
Affiliation:
1. Mathematics Department, Imperial College, London SW7 2EZ, UK
Publisher
Institute of Mathematical Statistics
Subject
Statistics and Probability
Reference115 articles.
1. J. Agler, J. E. McCarthy and N. Young, Operator analysis: Hilbert space methods in complex analysis. Cambridge Tracts Math. 219, Cambridge University Press, 2020.
2. A. Antoniadis, E. Paroditis and T. Sapatinas, A functional wavelet-kernel approach for time series prediction. J. Roy. Stat. Soc. B 68 (2006), 837-857.
3. A. Antoniadis and T. Sapatinas, Wavelet methods for continuous-time prediction using Hilbert-valued autoregressive processes. J. Multivariate Analysis 87 (2003), 133-158.
4. A. Aue and A. van Delft, Testing for stationarity of functional time series in the frequency domain. Ann. Statist. 48 (2020), 2505-2547; arXiv:1701.01741.
5. A. Aue, D. D. Norinho and S. Hörmann, On the prediction of stationary functional time series. J. Amer. Stat. Soc. 110 (2015), 378-392.
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