Time Series Clustering

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

1 leaderboard table shown for this task, 1 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

5 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

30 shown of 36 papers with code (113 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• SOM-VAE: Interpretable Discrete Representation Learning on Time Series
  6 Jun 2018 6 repositories listed Syntology ran 0 of 6 samples · 6 unverified
  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…
• PyPOTS: A Python Toolbox for Data Mining on Partially-Observed Time Series
  30 May 2023 5 repositories listed Syntology ran 0 of 21 samples · 21 unverified
  PyPOTS is an open-source Python library dedicated to data mining and analysis on multivariate partially-observed time series, i.
• N2D: (Not Too) Deep Clustering via Clustering the Local Manifold of an Autoencoded Embedding
  16 Aug 2019 5 repositories listed Syntology ran 2 of 4 samples · 2 unverified · 2 pointer-only (licence)
  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,…
• Forecasting Across Time Series Databases using Recurrent Neural Networks on Groups of Similar Series: A Clustering Approach
  9 Oct 2017 3 repositories listed
  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.
• Learning Representations for Time Series Clustering
  1 Dec 2019 2 repositories listed
  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.
• Deep learning for clustering of multivariate clinical patient trajectories with missing values
  15 Nov 2019 2 repositories listed
  Findings The problem of clustering multivariate short time series with many missing values is generally not well addressed in the literature.
• DPSOM: Deep Probabilistic Clustering with Self-Organizing Maps
  3 Oct 2019 2 repositories listed
  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.
• CSTS: A Benchmark for the Discovery of Correlation Structures in Time Series Clustering
  20 May 2025 1 repository listed
  To address these challenges, we introduce CSTS (Correlation Structures in Time Series), a synthetic benchmark for evaluating the discovery of correlation structures in multivariate time series data.
• k-Graph: A Graph Embedding for Interpretable Time Series Clustering
  18 Feb 2025 1 repository listed
  Time series clustering poses a significant challenge with diverse applications across domains.
• Time Series Clustering with General State Space Models via Stochastic Variational Inference
  29 Jun 2024 1 repository listed
  In this paper, we propose a novel method of model-based time series clustering with mixtures of general state space models (MSSMs).
• Unraveling Anomalies in Time: Unsupervised Discovery and Isolation of Anomalous Behavior in Bio-regenerative Life Support System Telemetry
  14 Jun 2024 1 repository listed
  The detection of abnormal or critical system states is essential in condition monitoring.
• ShapeDBA: Generating Effective Time Series Prototypes using ShapeDTW Barycenter Averaging
  28 Sep 2023 1 repository listed
  Our approach uses a new form of time series average, the ShapeDTW Barycentric Average.
• Graph-based Time Series Clustering for End-to-End Hierarchical Forecasting
  30 May 2023 1 repository listed Syntology ran 0 of 2 samples · 2 unverified
  Relationships among time series can be exploited as inductive biases in learning effective forecasting models.
• Time Series Clustering With Random Convolutional Kernels
  17 May 2023 1 repository listed
  The findings highlight the significance of R-Clustering in various domains and applications, contributing to the advancement of time series data mining.
• The Conditional Cauchy-Schwarz Divergence with Applications to Time-Series Data and Sequential Decision Making
  21 Jan 2023 1 repository listed Syntology ran 1 of 2 samples · 1 unverified · 2 pointer-only (licence)
  The Cauchy-Schwarz (CS) divergence was developed by Pr\'{i}ncipe et al.
• Unsupervised 4D LiDAR Moving Object Segmentation in Stationary Settings with Multivariate Occupancy Time Series
  30 Dec 2022 1 repository listed
  In this work, we address the problem of unsupervised moving object segmentation (MOS) in 4D LiDAR data recorded from a stationary sensor, where no ground truth annotations are involved.
• Uncertainty-DTW for Time Series and Sequences
  30 Oct 2022 1 repository listed Syntology ran 4 of 7 samples · 3 unverified · 7 pointer-only (licence)
  Dynamic Time Warping (DTW) is used for matching pairs of sequences and celebrated in applications such as forecasting the evolution of time series, clustering time series or even matching sequence pairs in few-shot…
• Time Series Clustering with an EM algorithm for Mixtures of Linear Gaussian State Space Models
  25 Aug 2022 1 repository listed
  In this paper, we consider the task of clustering a set of individual time series while modeling each cluster, that is, model-based time series clustering.
• Smart Data Collection System for Brownfield CNC Milling Machines: A New Benchmark Dataset for Data-Driven Machine Monitoring
  29 Jun 2022 1 repository listed
  To enhance the scalability of machine learning in real-world applications, this paper presents a benchmark dataset for process monitoring of brownfield milling machines based on acceleration data.
• TNN7: A Custom Macro Suite for Implementing Highly Optimized Designs of Neuromorphic TNNs
  16 May 2022 1 repository listed
  Recent works have proposed a microarchitecture framework for implementing TNNs and demonstrated competitive performance on vision and time-series applications.
• Novel Features for Time Series Analysis: A Complex Networks Approach
  11 Oct 2021 1 repository listed
  Our results are very promising, with network features from different mapping methods capturing different properties of the time series, adding a different and rich feature set to the literature.
• Learning Representations for Incomplete Time Series Clustering
  18 May 2021 1 repository listed
  Also, to reduce the error propagation from imputation to clustering, we introduce a discriminator to make the distribution of imputation values close to the true one and train CRLI in an alternating train- ing manner.
• Algorithms for Learning Graphs in Financial Markets
  31 Dec 2020 1 repository listed
  In the past two decades, the field of applied finance has tremendously benefited from graph theory.
• k-means on Positive Definite Matrices, and an Application to Clustering in Radar Image Sequences
  8 Aug 2020 1 repository listed
  We state theoretical properties for k-means clustering of Symmetric Positive Definite (SPD) matrices, in a non-Euclidean space, that provides a natural and favourable representation of these data.
• Conditional Latent Block Model: a Multivariate Time Series Clustering Approach for Autonomous Driving Validation
  3 Aug 2020 1 repository listed
  The FunCLBM model extends the recently proposed Functional Latent Block Model and allows to create a dependency structure between row and column clusters.
• Temporal Phenotyping using Deep Predictive Clustering of Disease Progression
  15 Jun 2020 1 repository listed
  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.
• Clustering Residential Electricity Consumption Data to Create Archetypes that Capture Household Behaviour in South Africa
  11 Jun 2020 1 repository listed
  While internal clustering validation measures are well established in the electricity domain, they are limited for selecting useful clusters.
• Deep Markov Spatio-Temporal Factorization
  22 Mar 2020 1 repository listed
  This results in a flexible family of hierarchical deep generative factor analysis models that can be extended to perform time series clustering or perform factor analysis in the presence of a control signal.
• Interpreting LSTM Prediction on Solar Flare Eruption with Time-series Clustering
  27 Dec 2019 1 repository listed
  The dynamics of these parameters have a highly uniform trajectory for many events whose LSTM prediction scores for M/X class flares transition from very low to very high.
• A time resolved clustering method revealing longterm structures and their short-term internal dynamics
  9 Dec 2019 1 repository listed
  The last decades have not only been characterized by an explosive growth of data, but also an increasing appreciation of data as a valuable resource.

Syntology lines on 6 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.