Time Series Clustering
30 papers with code • 1 benchmarks • 3 datasets
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
These leaderboards are used to track progress in Time Series Clustering
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Libraries
Use these libraries to find Time Series Clustering models and implementationsDatasets
UCR Time Series Classification Archive
Bosch CNC Machining Dataset
Most implemented papers
CRAD: Clustering with Robust Autocuts and Depth
We develop a new density-based clustering algorithm named CRAD which is based on a new neighbor searching function with a robust data depth as the dissimilarity measure.
Discovering patterns of online popularity from time series
By clustering the multidimensional time-series of the popularity of contents coupled with other domain-specific dimensions, we uncover two main patterns of popularity: bursty and steady temporal behaviors.
Linear Dynamics: Clustering without identification
Linear dynamical systems are a fundamental and powerful parametric model class.
A time resolved clustering method revealing longterm structures and their short-term internal dynamics
The last decades have not only been characterized by an explosive growth of data, but also an increasing appreciation of data as a valuable resource.
Interpreting LSTM Prediction on Solar Flare Eruption with Time-series Clustering
The dynamics of these parameters have a highly uniform trajectory for many events whose LSTM prediction scores for M/X class flares transition from very low to very high.
Deep Markov Spatio-Temporal Factorization
ostadabbas/Deep-Markov-Spatio-Temporal-Factorization-DMSTF-
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This results in a flexible family of hierarchical deep generative factor analysis models that can be extended to perform time series clustering or perform factor analysis in the presence of a control signal.
Clustering Residential Electricity Consumption Data to Create Archetypes that Capture Household Behaviour in South Africa
While internal clustering validation measures are well established in the electricity domain, they are limited for selecting useful clusters.
Temporal Phenotyping using Deep Predictive Clustering of Disease Progression
In this paper, we develop a deep learning approach for clustering time-series data, where each cluster comprises patients who share similar future outcomes of interest (e. g., adverse events, the onset of comorbidities).
Conditional Latent Block Model: a Multivariate Time Series Clustering Approach for Autonomous Driving Validation
The FunCLBM model extends the recently proposed Functional Latent Block Model and allows to create a dependency structure between row and column clusters.
$k$-means on Positive Definite Matrices, and an Application to Clustering in Radar Image Sequences
We state theoretical properties for $k$-means clustering of Symmetric Positive Definite (SPD) matrices, in a non-Euclidean space, that provides a natural and favourable representation of these data.
