# Model-based fuzzy time series clustering of conditional higher moments

arXiv: https://arxiv.org/abs/2104.00271 · HTML: https://mlsanity.com/paper/2104.00271/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Apr 2021.

## Built on by (most cited first, 2 of 2)

- [Quantile-based fuzzy C-means clustering of multivariate time series: Robust techniques](https://mlsanity.com/paper/2109.11027/) (2021)
- [New bootstrap tests for categorical time series. A comparative study](https://mlsanity.com/paper/2305.00465/) (2023)

## Abstract (arXiv)

This paper develops a new time series clustering procedure allowing for heteroskedasticity, non-normality and model's non-linearity. At this aim, we follow a fuzzy approach. Specifically, considering a Dynamic Conditional Score (DCS) model, we propose to cluster time series according to their estimated conditional moments via the Autocorrelation-based fuzzy C-means (A-FCM) algorithm. The DCS parametric modelling is appealing because of its generality and computational feasibility. The usefulness of the proposed procedure is illustrated using an experiment with simulated data and several empirical applications with financial time series assuming both linear and nonlinear models' specification and under several assumptions about time series density function.