Time Series Clustering

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 clusters and minimize it across clusters. Time-series clustering is often used as a subroutine of other more complex algorithms and is employed as a standard tool in data science for anomaly detection, character recognition, pattern discovery, visualization of time series. Source: Comprehensive Process Drift Detection with Visual Analytics

Benchmarks

Libraries

Datasets

Subtasks

Most implemented papers

N2D: (Not Too) Deep Clustering via Clustering the Local Manifold of an Autoencoded Embedding

Content

Forecasting across time series databases using recurrent neural networks on groups of similar series: A clustering approach

DPSOM: Deep Probabilistic Clustering with Self-Organizing Maps

Deep learning for clustering of multivariate clinical patient trajectories with missing values

Learning Representations for Time Series Clustering

Time series clustering via community detection in networks

Clustering Noisy Signals with Structured Sparsity Using Time-Frequency Representation

Applicability and interpretation of the deterministic weighted cepstral distance