# Deep Temporal Clustering : Fully Unsupervised Learning of Time-Domain Features

arXiv: https://arxiv.org/abs/1802.01059 · HTML: https://mlsanity.com/paper/1802.01059/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Feb 2018.

## Built on by (most cited first, 30 of 37)

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- [Longitudinal patient stratification of electronic health records with flexible adjustment for clinical outcomes](https://mlsanity.com/paper/2111.06152/) (2021)
- [SOM-CPC: Unsupervised Contrastive Learning with Self-Organizing Maps for Structured Representations of High-Rate Time Series](https://mlsanity.com/paper/2205.15875/) (2022)
- [Fuzzy Cluster-Aware Contrastive Clustering for Time Series](https://mlsanity.com/paper/2503.22211/) (2025)
- [Deep Spatiotemporal Clustering: A Temporal Clustering Approach for Multi-dimensional Climate Data](https://mlsanity.com/paper/2304.14541/) (2023)
- [THALIS: Human-Machine Analysis of Longitudinal Symptoms in Cancer Therapy](https://mlsanity.com/paper/2108.02817/) (2021)
- [KnAC: an approach for enhancing cluster analysis with background knowledge and explanations](https://mlsanity.com/paper/2112.08759/) (2021)
- [AUTOSHAPE: An Autoencoder-Shapelet Approach for Time Series Clustering](https://mlsanity.com/paper/2208.04313/) (2022)
- [Anomaly Detection in Event-triggered Traffic Time Series via Similarity Learning](https://mlsanity.com/paper/2506.16855/) (2025)
- [RandomNet: Clustering Time Series Using Untrained Deep Neural Networks](https://mlsanity.com/paper/2408.07956/) (2024)
- [Fitting Multiple Machine Learning Models with Performance Based Clustering](https://mlsanity.com/paper/2411.06572/) (2024)
- [k-Graph: A Graph Embedding for Interpretable Time Series Clustering](https://mlsanity.com/paper/2502.13049/) (2025)
- [B-TGAT: A Bi-directional Temporal Graph Attention Transformer for Clustering Multivariate Spatiotemporal Data](https://mlsanity.com/paper/2509.13202/) (2025)
- [Efficient Time Series Clustering from Multiscale Reservoir Dynamics with Granular-Ball Anchoring Graph Optimization](https://mlsanity.com/paper/2606.12077/) (2026)
- [Clustering of Time Series Data with Prior Geographical Information](https://mlsanity.com/paper/2107.01310/) (2021)
- [A multi-stage semi-supervised improved deep embedded clustering method for bearing fault diagnosis under the situation of insufficient labeled samples](https://mlsanity.com/paper/2109.13521/) (2021)
- [Task-aware Similarity Learning for Event-triggered Time Series](https://mlsanity.com/paper/2207.08159/) (2022)
- [EDGAR: Embedded Detection of Gunshots by AI in Real-time](https://mlsanity.com/paper/2211.14073/) (2022)
- [Multivariate Time Series Classification: A Deep Learning Approach](https://mlsanity.com/paper/2307.02253/) (2023)
- [Pain Forecasting using Self-supervised Learning and Patient Phenotyping: An attempt to prevent Opioid Addiction](https://mlsanity.com/paper/2310.06075/) (2023)
- [Delivery Optimized Discovery in Behavioral User Segmentation under Budget Constraint](https://mlsanity.com/paper/2402.03388/) (2024)
- [Towards Financially Inclusive Credit Products Through Financial Time Series Clustering](https://mlsanity.com/paper/2402.11066/) (2024)
- [Concrete Dense Network for Long-Sequence Time Series Clustering](https://mlsanity.com/paper/2405.05015/) (2024)
- [k-MLE, k-Bregman, k-VARs: Theory, Convergence, Computation](https://mlsanity.com/paper/2409.06938/) (2024)
- [Graphint: Graph-based Time Series Clustering Visualisation Tool](https://mlsanity.com/paper/2503.07698/) (2025)
- [CognitionNet: A Collaborative Neural Network for Play Style Discovery in Online Skill Gaming Platform](https://mlsanity.com/paper/2505.00325/) (2025)

## Abstract (arXiv)

Unsupervised learning of time series data, also known as temporal clustering, is a challenging problem in machine learning. Here we propose a novel algorithm, Deep Temporal Clustering (DTC), to naturally integrate dimensionality reduction and temporal clustering into a single end-to-end learning framework, fully unsupervised. The algorithm utilizes an autoencoder for temporal dimensionality reduction and a novel temporal clustering layer for cluster assignment. Then it jointly optimizes the clustering objective and the dimensionality reduction objec tive. Based on requirement and application, the temporal clustering layer can be customized with any temporal similarity metric. Several similarity metrics and state-of-the-art algorithms are considered and compared. To gain insight into temporal features that the network has learned for its clustering, we apply a visualization method that generates a region of interest heatmap for the time series. The viability of the algorithm is demonstrated using time series data from diverse domains, ranging from earthquakes to spacecraft sensor data. In each case, we show that the proposed algorithm outperforms traditional methods. The superior performance is attributed to the fully integrated temporal dimensionality reduction and clustering criterion.