Feature extraction for functional time series: Theory and application to NIR spectroscopy data

Yang Yang*, Yanrong Yang, Han Lin Shang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


We propose a novel method to extract global and local features of functional time series. The global features concerning the dominant modes of variation over the entire function domain, and local features of function variations over particular short intervals within function domain, are both important in functional data analysis. Functional principal component analysis (FPCA), though a key feature extraction tool, only focus on capturing the dominant global features, neglecting highly localized features. We introduce a FPCA-BTW method that initially extracts global features of functional data via FPCA, and then extracts local features by block thresholding of wavelet (BTW) coefficients. Using Monte Carlo simulations, along with an empirical application on near-infrared spectroscopy data of wood panels, we illustrate that the proposed method outperforms competing methods including FPCA and sparse FPCA in the estimation functional processes. Moreover, extracted local features inheriting serial dependence of the original functional time series contribute to more accurate forecasts. Finally, we develop asymptotic properties of FPCA-BTW estimators, discovering the interaction between convergence rates of global and local features.

Original languageEnglish
Article number104863
Pages (from-to)1-21
Number of pages21
JournalJournal of Multivariate Analysis
Early online date5 Nov 2021
Publication statusPublished - May 2022


  • Functional principal component analysis
  • Long-run covariance estimation
  • Near-infrared spectroscopy data
  • Regularized wavelet approximation


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