Sep 30, 2025 · Time series clustering is an unsupervised learning technique that groups data sequences collected over time based on their similarities. Unlike traditional clustering, it accounts for temporal …

To overcome the previously illustrated issue, distance metrics dedicated to time series, such as Dynamic Time Warping (DTW), are required. As can be seen in the Figure below, the use of such metrics …

Jul 28, 2021 · Time Series Clustering is an unsupervised data mining technique for organizing data points into groups based on their similarity. The objective is to maximize data similarity within …

These (dis)similarity measures are the cornerstone in time series classification and clustering. Once computed, we saw how they can be easily plugged into the same classification and clustering …

In this survey, we trace the evolution of time-series clustering methods from classical approaches to recent advances in neural networks.

To demonstrate the robustness of SBD, k-Shape, and k-MS, we perform an extensive experimental evaluation on 85 datasets against state-of-the-art distance measures and clustering methods for time …

Oct 1, 2015 · This review will expose four main components of time-series clustering and is aimed to represent an updated investigation on the trend of improvements in efficiency, quality and complexity …

Jul 1, 2025 · To address these gaps, we evaluate 84 time-series clustering methods across 10 method classes from data mining, machine learning, and deep learning. Our analysis spans 128 time-series …

To address these gaps, we eval-uate 84 time-series clustering methods across 10 method classes from data mining, machine learning, and deep learning. Our anal-ysis spans 128 time-series datasets and …

Time series clustering is to partition time series data into groups based on similarity or distance, so that time series in the same cluster are similar.