Annals of Statistics

Fourier analysis of stationary time series in function space

Victor M. Panaretos and Shahin Tavakoli

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We develop the basic building blocks of a frequency domain framework for drawing statistical inferences on the second-order structure of a stationary sequence of functional data. The key element in such a context is the spectral density operator, which generalises the notion of a spectral density matrix to the functional setting, and characterises the second-order dynamics of the process. Our main tool is the functional Discrete Fourier Transform (fDFT). We derive an asymptotic Gaussian representation of the fDFT, thus allowing the transformation of the original collection of dependent random functions into a collection of approximately independent complex-valued Gaussian random functions. Our results are then employed in order to construct estimators of the spectral density operator based on smoothed versions of the periodogram kernel, the functional generalisation of the periodogram matrix. The consistency and asymptotic law of these estimators are studied in detail. As immediate consequences, we obtain central limit theorems for the mean and the long-run covariance operator of a stationary functional time series. Our results do not depend on structural modelling assumptions, but only functional versions of classical cumulant mixing conditions, and are shown to be stable under discrete observation of the individual curves.

Article information

Ann. Statist., Volume 41, Number 2 (2013), 568-603.

First available in Project Euclid: 26 April 2013

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Digital Object Identifier

Mathematical Reviews number (MathSciNet)

Zentralblatt MATH identifier

Primary: 62M10: Time series, auto-correlation, regression, etc. [See also 91B84]
Secondary: 62M15: Spectral analysis 60G10: Stationary processes

Cumulants discrete Fourier transform functional data analysis functional time series periodogram operator spectral density operator weak dependence


Panaretos, Victor M.; Tavakoli, Shahin. Fourier analysis of stationary time series in function space. Ann. Statist. 41 (2013), no. 2, 568--603. doi:10.1214/13-AOS1086.

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Supplemental materials

  • Supplementary material: “Fourier Analysis of Stationary Time Series in Function Space”. The online supplement contains the proofs that were omitted, and several additional technical results used in this paper.