Robustness in Time Series: Robust Frequency Domain Analysis

Robustness in time series analysis is an important issue. A motivation for robust frequency domain analysis can be taken from medical application: Short-term heart rate variability recordings are usually analyzed in the frequency domain. The heart rate variability is assessed by estimating the spectral density function of the tachogram series. It is well known that classical spectral density estimates are prone to outlying observations, hence, robustness is an issue. The presented multi-step procedure based on robust filtering is insensitive to outliers, and therefore provides fully automated signal processing which will facilitate reliable and reproducible heart rate variability analysis with minimal operator input. Moreover, it can also be used to identify and mark outlying observations.

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