# Skip-sampling: subsampling in the frequency domain

arXiv: https://arxiv.org/abs/2212.08160 · HTML: https://mlsanity.com/paper/2212.08160/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Dec 2022.

## Built on by (most cited first, 1 of 1)

- [ProbRes: Volatility Learning for Probabilistic Time-Series Forecasting](https://mlsanity.com/paper/2606.02117/) (2026)

## Abstract (arXiv)

Over the last 35 years, several bootstrap methods for time series have been proposed. Popular `time-domain' methods include the block-bootstrap, the stationary bootstrap, the linear process bootstrap, etc.; subsampling for time series is also available, and is closely related to the block-bootstrap. `Frequency-domain' bootstrap has been performed either by resampling the periodogram ordinates or by resampling the ordinates of the Discrete Fourier Transform (DFT). The paper at hand proposes a novel construction of subsampling the DFT ordinates, and investigates its theoretical properties and realm of applicability.