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
Latest papers with no code
Clustering Method for Time-Series Images Using Quantum-Inspired Computing Technology
In contrast, the clustering results by the standard existing method and the conditional image sampling (CIS) method, a specialized technique for flow measurement data, displayed overlapping clusters.
Time series clustering based on prediction accuracy of global forecasting models
In this paper, a novel method to perform model-based clustering of time series is proposed.
Fuzzy clustering of ordinal time series based on two novel distances with economic applications
In this paper, the problem of clustering ordinal time series is addressed.
SE-shapelets: Semi-supervised Clustering of Time Series Using Representative Shapelets
Shapelets that discriminate time series using local features (subsequences) are promising for time series clustering.
Interpretable Deep Learning for Forecasting Online Advertising Costs: Insights from the Competitive Bidding Landscape
As advertisers increasingly shift their budgets toward digital advertising, forecasting advertising costs is essential for making budget plans to optimize marketing campaign returns.
The Conditional Cauchy-Schwarz Divergence with Applications to Time-Series Data and Sequential Decision Making
In this paper, we extend the classic CS divergence to quantify the closeness between two conditional distributions and show that the developed conditional CS divergence can be simply estimated by a kernel density estimator from given samples.
Deep Temporal Contrastive Clustering
Recently the deep learning has shown its advantage in representation learning and clustering for time series data.
Large-scale unsupervised spatio-temporal semantic analysis of vast regions from satellite images sequences
In this context, we present a fully unsupervised and general methodology to conduct spatio-temporal taxonomies of large regions from sequences of satellite images.
AUTOSHAPE: An Autoencoder-Shapelet Approach for Time Series Clustering
To study the performance of multivariate time series (MTS), we evaluate AUTOSHAPE on 30 UEA archive datasets with 5 competitive methods.
Interpretable Time Series Clustering Using Local Explanations
The explanations are used to obtain insights into the clustering models.
