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
Interpretable Time Series Clustering Using Local Explanations
The explanations are used to obtain insights into the clustering models.
K-ARMA Models for Clustering Time Series Data
We then apply our method first with an AR($p$) clustering example and show how the clustering algorithm can be made robust to outliers using a least-absolute deviations criteria.
A Review and Evaluation of Elastic Distance Functions for Time Series Clustering
Our conclusion is to recommend MSM with k-medoids as the benchmark algorithm for clustering time series with elastic distance measures.
Towards a Design Framework for TNN-Based Neuromorphic Sensory Processing Units
Temporal Neural Networks (TNNs) are spiking neural networks that exhibit brain-like sensory processing with high energy efficiency.
Time Series Clustering for Grouping Products Based on Price and Sales Patterns
We compare our approach with traditional clustering algorithms, which typically rely on generic distance metrics such as Euclidean distance, and image clustering approaches that aim to group data by capturing its visual patterns.
Detecting CAN Masquerade Attacks with Signal Clustering Similarity
Specifically, we demonstrate that masquerade attacks can be detected by computing time series clustering similarity using hierarchical clustering on the vehicle's CAN signals (time series) and comparing the clustering similarity across CAN captures with and without attacks.
Hydroclimatic time series features at multiple time scales
A comprehensive understanding of the behaviours of the various geophysical processes and an effective evaluation of time series (else referred to as "stochastic") simulation models require, among others, detailed investigations across temporal scales.
Unsupervised Visual Time-Series Representation Learning and Clustering
Time-series data is generated ubiquitously from Internet-of-Things (IoT) infrastructure, connected and wearable devices, remote sensing, autonomous driving research and, audio-video communications, in enormous volumes.
Coresets for Time Series Clustering
In particular, we consider the setting where the time series data on $N$ entities is generated from a Gaussian mixture model with autocorrelations over $k$ clusters in $\mathbb{R}^d$.
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