# FCPCA: Fuzzy clustering of high-dimensional time series based on common principal component analysis

arXiv: https://arxiv.org/abs/2505.07276 · HTML: https://mlsanity.com/paper/2505.07276/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 100 references that are in the map.
- First posted on arXiv 12 May 2025.

## Builds on (most cited first, 12 of 12)

- [Graph Neural Network-Based Anomaly Detection in Multivariate Time Series](https://mlsanity.com/paper/2106.06947/) (2021)
- [Unsupervised Scalable Representation Learning for Multivariate Time Series](https://mlsanity.com/paper/1901.10738/) (2019)
- [Benchmarking Multivariate Time Series Classification Algorithms](https://mlsanity.com/paper/2007.13156/) (2020)
- [A Benchmark Study on Time Series Clustering](https://mlsanity.com/paper/2004.09546/) (2020)
- [High-Dimensional Granger Causality Tests with an Application to VIX and News](https://mlsanity.com/paper/1912.06307/) (2019)
- [A Compact and Interpretable Convolutional Neural Network for Cross-Subject Driver Drowsiness Detection from Single-Channel EEG](https://mlsanity.com/paper/2106.00613/) (2021)
- [SageFormer: Series-Aware Framework for Long-term Multivariate Time Series Forecasting](https://mlsanity.com/paper/2307.01616/) (2023)
- [Spectral Dependence](https://mlsanity.com/paper/2103.17240/) (2021)
- [Quantile-based fuzzy clustering of multivariate time series in the frequency domain](https://mlsanity.com/paper/2109.03728/) (2021)
- [Quantile-based fuzzy C-means clustering of multivariate time series: Robust techniques](https://mlsanity.com/paper/2109.11027/) (2021)
- [Kernel Two-Dimensional Ridge Regression for Subspace Clustering](https://mlsanity.com/paper/2011.01477/) (2020)
- [Vector AutoRegressive Moving Average Models: A Review](https://mlsanity.com/paper/2406.19702/) (2024)

## Built on by (most cited first, 5 of 5)

- [Robust fuzzy clustering for high-dimensional multivariate time series with outlier detection](https://mlsanity.com/paper/2510.26982/) (2025)
- [Forecasting Multivariate Time Series under Predictive Heterogeneity: A Validation-Driven Clustering Framework](https://mlsanity.com/paper/2604.13748/) (2026)
- [CPCANet: Deep Unfolding Common Principal Component Analysis for Domain Generalization](https://mlsanity.com/paper/2605.05136/) (2026)
- [Robust Spectral Fuzzy Clustering of Multivariate Time Series with Applications to Electroencephalogram](https://mlsanity.com/paper/2506.22861/) (2025)
- [Canonical Tail Dependence for Soft Extremal Clustering of Multichannel Brain Signals](https://mlsanity.com/paper/2512.06435/) (2025)

## Abstract (arXiv)

Clustering multivariate time series data is a crucial task in many domains, as it enables the identification of meaningful patterns and groups in time-evolving data. Traditional approaches, such as crisp clustering, rely on the assumption that clusters are sufficiently separated with little overlap. However, real-world data often defy this assumption, exhibiting overlapping distributions or overlapping clouds of points and blurred boundaries between clusters. Fuzzy clustering offers a compelling alternative by allowing partial membership in multiple clusters, making it well-suited for these ambiguous scenarios. Despite its advantages, current fuzzy clustering methods primarily focus on univariate time series, and for multivariate cases, even datasets of moderate dimensionality become computationally prohibitive. This challenge is further exacerbated when dealing with time series of varying lengths, leaving a clear gap in addressing the complexities of modern datasets. This work introduces a novel fuzzy clustering approach based on common principal component analysis to address the aforementioned shortcomings. Our method has the advantage of efficiently handling high-dimensional multivariate time series by reducing dimensionality while preserving critical temporal features. Extensive numerical results show that our proposed clustering method outperforms several existing approaches in the literature. An interesting application involving brain signals from different drivers recorded from a simulated driving experiment illustrates the potential of the approach.