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

## TL;DR

This paper introduces a robust clustering method for stationary time series that leverages spectral densities as functional data, employing trimming and scatter restrictions to enhance noise resistance and prevent spurious clusters.

## Contribution

It proposes a novel clustering algorithm using spectral densities as functional data with robustness techniques for stationary time series.

## Key findings

- Effective in reducing noise impact
- Prevents spurious cluster detection
- Validated through simulation and real data

## Abstract

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.