Time Series Clustering

17 papers with code • 1 benchmarks • 1 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

Greatest papers with code

DPSOM: Deep Probabilistic Clustering with Self-Organizing Maps

ratschlab/SOM-VAE 3 Oct 2019

We show that DPSOM achieves superior clustering performance compared to current deep clustering methods on MNIST/Fashion-MNIST, while maintaining the favourable visualization properties of SOMs.

Clustering Deep Clustering +4

SOM-VAE: Interpretable Discrete Representation Learning on Time Series

ratschlab/SOM-VAE ICLR 2019

We evaluate our model in terms of clustering performance and interpretability on static (Fashion-)MNIST data, a time series of linearly interpolated (Fashion-)MNIST images, a chaotic Lorenz attractor system with two macro states, as well as on a challenging real world medical time series application on the eICU data set.

Clustering Dimensionality Reduction +3

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

rymc/n2d 16 Aug 2019

We study a number of local and global manifold learning methods on both the raw data and autoencoded embedding, concluding that UMAP in our framework is best able to find the most clusterable manifold in the embedding, suggesting local manifold learning on an autoencoded embedding is effective for discovering higher quality discovering clusters.

Clustering Deep Clustering +4

Time Series Clustering via Community Detection in Networks

lnferreira/time_series_clustering_via_community_detection 19 Aug 2015

In this paper, we propose a technique for time series clustering using community detection in complex networks.

Clustering Community Detection +3

Temporal Phenotyping using Deep Predictive Clustering of Disease Progression

chl8856/AC_TPC ICML 2020

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).

Clustering Decision Making +2

Algorithms for Learning Graphs in Financial Markets

mirca/fingraph 31 Dec 2020

In the past two decades, the field of applied finance has tremendously benefited from graph theory.

Graph Learning Time Series +1

Discovering patterns of online popularity from time series

mertozer/mts-clustering 10 Apr 2019

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.

Clustering Time Series +1

Learning Representations for Time Series Clustering

KMdsy/DTCR NeurIPS 2019

When applying seq2seq to time series clustering, obtaining a representation that effectively represents the temporal dynamics of the sequence, multi-scale features, and good clustering properties remains a challenge.

Anomaly Detection Clustering +3

Forecasting Across Time Series Databases using Recurrent Neural Networks on Groups of Similar Series: A Clustering Approach

EvgeniyaMartynova/MLiP_M5 9 Oct 2017

In particular, in terms of mean sMAPE accuracy, it consistently outperforms the baseline LSTM model and outperforms all other methods on the CIF2016 forecasting competition dataset.

Clustering Time Series +2

Conditional Latent Block Model: a Multivariate Time Series Clustering Approach for Autonomous Driving Validation

EtienneGof/FunCLBM 3 Aug 2020

The FunCLBM model extends the recently proposed Functional Latent Block Model and allows to create a dependency structure between row and column clusters.

Autonomous Driving Clustering +5