Towards Explainable Deep Clustering for Time Series Data

• URL: http://arxiv.org/abs/2507.20840v2
• Date: Mon, 20 Oct 2025 08:20:05 GMT
• Title: Towards Explainable Deep Clustering for Time Series Data
• Abstract summary: This survey offers a structured overview of explainable deep clustering for time series.<n>Our analysis reveals that most work relies on autoencoder and attention architectures, with limited support for streaming, irregularly sampled, or privacy-preserved series.<n>To push the field forward, we outline six research opportunities.
• Score: 5.031537780234399
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deep clustering uncovers hidden patterns and groups in complex time series data, yet its opaque decision-making limits use in safety-critical settings. This survey offers a structured overview of explainable deep clustering for time series, collecting current methods and their real-world applications. We thoroughly discuss and compare peer-reviewed and preprint papers through application domains across healthcare, finance, IoT, and climate science. Our analysis reveals that most work relies on autoencoder and attention architectures, with limited support for streaming, irregularly sampled, or privacy-preserved series, and interpretability is still primarily treated as an add-on. To push the field forward, we outline six research opportunities: (1) combining complex networks with built-in interpretability; (2) setting up clear, faithfulness-focused evaluation metrics for unsupervised explanations; (3) building explainers that adapt to live data streams; (4) crafting explanations tailored to specific domains; (5) adding human-in-the-loop methods that refine clusters and explanations together; and (6) improving our understanding of how time series clustering models work internally. By making interpretability a primary design goal rather than an afterthought, we propose the groundwork for the next generation of trustworthy deep clustering time series analytics.

Related papers

• Object Counting Across Modalities: Taxonomies, Benchmarks, Applications, and Open Challenges [3.1411048327150546]
  We propose a roadmap for compositional scene understanding, active counting agents, and unified multimodal evaluation protocols.<n>The main imperative is to build a robust evaluation infrastructure to distinguish open-world generalization from benchmark-specific optimization.
  arXiv  Detail & Related papers  (2026-08-24T21:32:56Z)
• ProtoTSNet: Interpretable Multivariate Time Series Classification With Prototypical Parts [6.99674326582747]
  ProtoTSNet is a novel approach to interpretable classification of time series data.<n>Central to our innovation is a modified convolutional encoder utilizing group convolutions, pre-trainable as part of an autoencoder.
  arXiv  Detail & Related papers  (2025-11-04T00:42:42Z)
• TimeOmni-1: Incentivizing Complex Reasoning with Time Series in Large Language Models [105.47481207029047]
  We introduce Time Series Reasoning Suite (TSR-Suite), which formalizes four atomic tasks that span three fundamental capabilities for reasoning with time series.<n>We also introduce Time Omni-1, the first unified reasoning model designed to address diverse real-world problems demanding time series reasoning.
  arXiv  Detail & Related papers  (2025-09-29T13:54:34Z)
• Time-RA: Towards Time Series Reasoning for Anomaly with LLM Feedback [55.284574165467525]
  Time-series Reasoning for Anomaly (Time-RA) transforms classical time series anomaly detection into a generative, reasoning-intensive task.<n>Also, we introduce the first real-world multimodal benchmark dataset, RATs40K, explicitly annotated for anomaly reasoning.
  arXiv  Detail & Related papers  (2025-07-20T18:02:50Z)
• When Does Multimodality Lead to Better Time Series Forecasting? [96.26052272121615]
  We investigate whether and under what conditions such multimodal integration consistently yields gains.<n>Our findings reveal that the benefits of multimodality are highly condition-dependent.<n>Our study offers a rigorous, quantitative foundation for understanding when multimodality can be expected to aid forecasting tasks.
  arXiv  Detail & Related papers  (2025-06-20T23:55:56Z)
• Bridging the Gap: A Decade Review of Time-Series Clustering Methods [4.318028678596998]
  Time-series data presents significant challenges for analyzing latent structures over extended temporal scales.<n>Time-series clustering is an established unsupervised learning strategy that groups similar time series together.<n>This survey highlights key developments and provides insights to guide future research in time-series clustering.
  arXiv  Detail & Related papers  (2024-12-29T21:04:35Z)
• A Survey on Diffusion Models for Time Series and Spatio-Temporal Data [92.1255811066468]
  We review the use of diffusion models in time series and S-temporal data, categorizing them by model, task type, data modality, and practical application domain. We categorize diffusion models into unconditioned and conditioned types discuss time series and S-temporal data separately. Our survey covers their application extensively in various fields including healthcare, recommendation, climate, energy, audio, and transportation.
  arXiv  Detail & Related papers  (2024-04-29T17:19:40Z)
• Contrastive Continual Multi-view Clustering with Filtered Structural Fusion [57.193645780552565]
  Multi-view clustering thrives in applications where views are collected in advance. It overlooks scenarios where data views are collected sequentially, i.e., real-time data. Some methods are proposed to handle it but are trapped in a stability-plasticity dilemma. We propose Contrastive Continual Multi-view Clustering with Filtered Structural Fusion.
  arXiv  Detail & Related papers  (2023-09-26T14:18:29Z)
• Temporal Saliency Detection Towards Explainable Transformer-based Timeseries Forecasting [3.046315755726937]
  This paper introduces Temporal Saliency Detection (TSD), an effective approach that builds upon the attention mechanism and applies it to multi-horizon time series prediction. The TSD approach facilitates the multiresolution analysis of saliency patterns by condensing multi-heads, thereby progressively enhancing the forecasting of complex time series data.
  arXiv  Detail & Related papers  (2022-12-15T12:47:59Z)
• A Deep Dive into Deep Cluster [0.2578242050187029]
  DeepCluster is a simple and scalable unsupervised pretraining of visual representations. We show that DeepCluster convergence and performance depend on the interplay between the quality of the randomly filters of the convolutional layer and the selected number of clusters.
  arXiv  Detail & Related papers  (2022-07-24T22:55:09Z)
• An Empirical Study: Extensive Deep Temporal Point Process [61.14164208094238]
  We first review recent research emphasis and difficulties in modeling asynchronous event sequences with deep temporal point process.<n>We propose a Granger causality discovery framework for exploiting the relations among multi-types of events.
  arXiv  Detail & Related papers  (2021-10-19T10:15:00Z)
• Time Series Analysis via Network Science: Concepts and Algorithms [62.997667081978825]
  This review provides a comprehensive overview of existing mapping methods for transforming time series into networks. We describe the main conceptual approaches, provide authoritative references and give insight into their advantages and limitations in a unified notation and language. Although still very recent, this research area has much potential and with this survey we intend to pave the way for future research on the topic.
  arXiv  Detail & Related papers  (2021-10-11T13:33:18Z)
• Author Clustering and Topic Estimation for Short Texts [69.54017251622211]
  We propose a novel model that expands on the Latent Dirichlet Allocation by modeling strong dependence among the words in the same document. We also simultaneously cluster users, removing the need for post-hoc cluster estimation. Our method performs as well as -- or better -- than traditional approaches to problems arising in short text.
  arXiv  Detail & Related papers  (2021-06-15T20:55:55Z)
• Deep learning for time series classification [2.0305676256390934]
  Time series analysis allows us to visualize and understand the evolution of a process over time. Time series classification consists of constructing algorithms dedicated to automatically label time series data. Deep learning has emerged as one of the most effective methods for tackling the supervised classification task.
  arXiv  Detail & Related papers  (2020-10-01T17:38:40Z)
• Predicting Temporal Sets with Deep Neural Networks [50.53727580527024]
  We propose an integrated solution based on the deep neural networks for temporal sets prediction. A unique perspective is to learn element relationship by constructing set-level co-occurrence graph. We design an attention-based module to adaptively learn the temporal dependency of elements and sets.
  arXiv  Detail & Related papers  (2020-06-20T03:29:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.