Time Series Clustering
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Benchmarks
Most implemented
SOM-VAE: Interpretable Discrete Representation Learning on Time Series
PyPOTS: A Python Toolbox for Data Mining on Partially-Observed Time Series
N2D: (Not Too) Deep Clustering via Clustering the Local Manifold of an Autoencoded Embedding
Forecasting Across Time Series Databases using Recurrent Neural Networks on Groups of Similar Series: A Clustering Approach
Learning Representations for Time Series Clustering
Papers
Efficient Time Series Clustering from Multiscale Reservoir Dynamics with Granular-Ball Anchoring Graph Optimization
Time-series clustering remains challenging due to the inherent trade-off between clustering effectiveness and computational efficiency. Similarity-based methods often suffer from quadratic complexity caused by pairwise d…
Computational EfficiencyTime Series ClusteringAmortized Neural Clustering of Time Series based on Statistical Features
This paper introduces an algorithm-agnostic approach to feature-based time series clustering via amortized neural inference. By training neural networks to approximate the optimal partitioning rule from simulated data, t…
Time Series ClusteringScalable inference of spatial regions and temporal signatures from time series
Regionalization aims to partition a spatial domain into contiguous regions that share similar characteristics, enabling more effective spatial analysis, policy making, and resource management. Existing approaches for spa…
Time Series ClusteringLDTC: Lifelong deep temporal clustering for multivariate time series
Clustering temporal and dynamically changing multivariate time series from real-world fields, called temporal clustering for short, has been a major challenge due to inherent complexities. Although several deep temporal …
Dimensionality ReductionTime Series ClusteringA Quantum Tensor Network-Based Viewpoint for Modeling and Analysis of Time Series Data
Accurate uncertainty quantification is a critical challenge in machine learning. While neural networks are highly versatile and capable of learning complex patterns, they often lack interpretability due to their ``black …
Change Point DetectionTime Series ClusteringImproving Internet Traffic Matrix Prediction via Time Series Clustering
We present a novel framework that leverages time series clustering to improve internet traffic matrix (TM) prediction using deep learning (DL) models. Traffic flows within a TM often exhibit diverse temporal behaviors, w…
Time Series Clustering