# Bridging the Gap: A Decade Review of Time-Series Clustering Methods

arXiv: https://arxiv.org/abs/2412.20582 · HTML: https://mlsanity.com/paper/2412.20582/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Dec 2024.

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

- [Terminal Dimension Reduction for Time Series with Applications](https://mlsanity.com/paper/2607.09490/) (2026)
- [CLUES-WEASEL: No additional clues required to choose your time series clustering algorithm](https://mlsanity.com/paper/2609.07606/) (2026)

## Abstract (arXiv)

Time series, as one of the most fundamental representations of sequential data, has been extensively studied across diverse disciplines, including computer science, biology, geology, astronomy, and environmental sciences. The advent of advanced sensing, storage, and networking technologies has resulted in high-dimensional time-series data, however, posing significant challenges for analyzing latent structures over extended temporal scales. Time-series clustering, an established unsupervised learning strategy that groups similar time series together, helps unveil hidden patterns in these complex datasets. In this survey, we trace the evolution of time-series clustering methods from classical approaches to recent advances in neural networks. While previous surveys have focused on specific methodological categories, we bridge the gap between traditional clustering methods and emerging deep learning-based algorithms, presenting a comprehensive, unified taxonomy for this research area. This survey highlights key developments and provides insights to guide future research in time-series clustering.