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- W4383472977 abstract "A novel method for noise reduction in the setting of curve time series with error contamination is proposed, based on extending the framework of functional principal component analysis (FPCA). We employ the underlying, finite-dimensional dynamics of the functional time series to separate the serially dependent dynamical part of the observed curves from the noise. Upon identifying the subspaces of the signal and idiosyncratic components, we construct a projection of the observed curve time series along the noise subspace, resulting in an estimate of the underlying denoised curves. This projection is optimal in the sense that it minimizes the mean integrated squared error. By applying our method to similated and real data, we show the denoising estimator is consistent and outperforms existing denoising techniques. Furthermore, we show it can be used as a pre-processing step to improve forecasting." @default.
- W4383472977 created "2023-07-07" @default.
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- W4383472977 date "2023-07-05" @default.
- W4383472977 modified "2023-09-24" @default.
- W4383472977 title "Noise reduction for functional time series" @default.
- W4383472977 doi "https://doi.org/10.48550/arxiv.2307.02154" @default.
- W4383472977 hasPublicationYear "2023" @default.
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