# Robust Clustering for Time Series Using Spectral Densities and Functional Data Analysis

arXiv: https://arxiv.org/abs/1702.02165 · HTML: https://mlsanity.com/paper/1702.02165/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Feb 2017.

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

- [Quantile-based fuzzy C-means clustering of multivariate time series: Robust techniques](https://mlsanity.com/paper/2109.11027/) (2021)

## Abstract (arXiv)

In this work a robust clustering algorithm for stationary time series is proposed. The algorithm is based on the use of estimated spectral densities, which are considered as functional data, as the basic characteristic of stationary time series for clustering purposes. A robust algorithm for functional data is then applied to the set of spectral densities. Trimming techniques and restrictions on the scatter within groups reduce the effect of noise in the data and help to prevent the identification of spurious clusters. The procedure is tested in a simulation study, and is also applied to a real data set.