# A Portmanteau-type test for detecting serial correlation in locally stationary functional time series

arXiv: https://arxiv.org/abs/2009.07312 · HTML: https://mlsanity.com/paper/2009.07312/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Sep 2020.

## Built on by (most cited first, 3 of 3)

- [Functional Neural Networks: Shift invariant models for functional data with applications to EEG classification](https://mlsanity.com/paper/2301.05869/) (2023)
- [GT-PCA: Effective and Interpretable Dimensionality Reduction with General Transform-Invariant Principal Component Analysis](https://mlsanity.com/paper/2401.15623/) (2024)
- [EEG-EyeTrack: A Benchmark for Time Series and Functional Data Analysis with Open Challenges and Baselines](https://mlsanity.com/paper/2504.03760/) (2025)

## Abstract (arXiv)

The Portmanteau test provides the vanilla method for detecting serial correlations in classical univariate time series analysis. The method is extended to the case of observations from a locally stationary functional time series. Asymptotic critical values are obtained by a suitable block multiplier bootstrap procedure. The test is shown to asymptotically hold its level and to be consistent against general alternatives.