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


Use these libraries to find Time Series Clustering models and implementations
3 papers

Most implemented papers

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.

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.

PyPOTS: A Python Toolbox for Data Mining on Partially-Observed Time Series

WenjieDu/PyPOTS 30 May 2023

PyPOTS is an open-source Python library dedicated to data mining and analysis on multivariate partially-observed time series, i. e. incomplete time series with missing values, A. K. A.

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.

DPSOM: Deep Probabilistic Clustering with Self-Organizing Maps

ratschlab/dpsom 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.

Deep learning for clustering of multivariate clinical patient trajectories with missing values

johanndejong/vader_supporting_code GigaScience 2019

Findings The problem of clustering multivariate short time series with many missing values is generally not well addressed in the literature.

Learning Representations for Time Series Clustering

qianlima-lab/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.

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 Noisy Signals with Structured Sparsity Using Time-Frequency Representation

avishaiwa/SPARCWave 18 Oct 2015

We propose a simple and efficient time-series clustering framework particularly suited for low Signal-to-Noise Ratio (SNR), by simultaneous smoothing and dimensionality reduction aimed at preserving clustering information.

Applicability and interpretation of the deterministic weighted cepstral distance

Olauwers/Applicability-and-interpretation-of-the-deterministic-weighted-cepstral-distance 8 Mar 2018

In this way, we provide a purely data-driven way to assess different underlying dynamics of input/output signal pairs, without the need for any system identification step.