Gao, Yuan; Shang, Hanlin; Yang, Yanrong
In this paper, we address the problem of forecasting high-dimensional functional time series through a two-fold dimension reduction procedure. Dynamic functional principal component analysis is applied to reduce each infinite-dimension functional time series to a vector. We use factor model as a further dimension reduction technique so that only a small number of latent factors are preserved. Simple time series models can be used to forecast the factors and forecast of the functions can be...[Show more]
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