Awesome Time Series Segmentation Papers

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Description

This repository contains a reading list of papers on Time Series Segmentation. This repository is still being continuously improved.

As a crucial time series preprocessing technique, semantic segmentation divides poorly understood time series into several discrete and homogeneous segments. This approach aims to uncover latent temporal evolution patterns, detect unexpected regularities and regimes, thereby rendering the analysis of massive time series data more manageable.

Time series segmentation often intertwines with research in many domains. Firstly, the relationship between time series segmentation, time series change point detection, and some aspects of time series anomaly/outlier detection is somewhat ambiguous. Therefore, this repository includes a selection of papers from these areas. Secondly, time series segmentation can be regarded as a process of information compression in time series, hence papers in this field often incorporate concepts from information theory (e.g., using minimum description length to guide the design of unsupervised time series segmentation models). Additionally, the task of decomposing human actions into a series of plausible motion primitives can be addressed through methods for segmenting sensor time series. Consequently, papers related to motion capture from the fields of computer vision and ubiquitous computing are also included in this collection.

Generally, the subjects of unsupervised semantic segmentation can be categorized into:

• univariate time series: , where is the length of the time series.
• multivariate time series: , where is the number of variables (channels).
• tensor: , where denotes the dimensions other than time and variables.

In the field of time series research, unlike time series forecasting, anomaly detection, and classification/clustering, the number of papers on time series segmentation has been somewhat lukewarm in recent years (this observation may carry a degree of subjectivity from the author). Additionally, deep learning methods do not seem to dominate this area as they do in others. Some classic but solid algorithms remain highly competitive even today, with quite a few originating from the same research group. Therefore, in the following paper list, I will introduce them indexed by well-known researchers and research groups in this field.

Citation

🚩 2026/4/5: If you find this repository helpful for your research, please consider citing the following paper:

Li, Z., & Wang, L. (2026). JT3D: Joint temporal segmentation for common pattern discovery in 3D tensor time series via segmented Gaussian modeling and information-gain. Neurocomputing, 682, 133453. https://doi.org/10.1016/j.neucom.2026.133453

@article{LI2026133453,
  title = {JT3D: Joint temporal segmentation for common pattern discovery in 3D tensor time series via segmented Gaussian modeling and information-gain},
  author = {Zezhong Li and Ling Wang},
  journal = {Neurocomputing},
  volume = {682},
  pages = {133453},
  year = {2026},
  issn = {0925-2312},
  doi = {10.1016/j.neucom.2026.133453},
  url = {https://www.sciencedirect.com/science/article/pii/S0925231226008507}
}

Some Additional Information

🚩 2024/4/28: In fact, manually annotating segment points (change points) in large time series datasets is extremely labor-intensive and somewhat subjective. Therefore, the field of time series segmentation lacks large public datasets with ground truth, making it difficult for supervised methods to find sources of training data. Unsupervised time series segmentation also acts to some extent as an automatic annotator of segmentation points, making it easier to implement. Currently, 95% of the research work included in this repository is unsupervised.

🚩 2024/1/27: I have marked some recommended papers / datasets / implementations with 🌟 (Just my personal preference 😉).

Survey & Evaluation

NOTE: the ranking has no particular order.

TYPE	Venue	Paper Title and Paper Interpretation	Code
Survey	Front. Eng. Manag. '25	Change-point detection with deep learning: A review	None
Survey	J R STAT SOC B. (JRSSB) '24	Automatic change-point detection in time series via deep learning	AutoCPD
Survey	Comput. Mater. Con. '24	Unsupervised Time Series Segmentation: A Survey on Recent Advances	None
Dataset	DARLI-AP@EDBT/ICDT '23	Time Series Segmentation Applied to a New Data Set for Mobile Sensing of Human Activities 🌟	MOSAD
Dataset	ECML-PKDD Workshop '23	Human Activity Segmentation Challenge@ECML/PKDD’23 🌟	Challenge Link
Visualization	IEEE TVCG '21	MultiSegVA Using Visual Analytics to Segment Biologging Time Series on Multiple Scales	None
Survey	IEEE J. Sel. Areas Commun. '21	Sequential (Quickest) Change Detection Classical Results and New Directions	None
Survey	Signal Process. '20	Selective review of offline change point detection methods 🌟

David Hallac (Stanford)

TYPE	Venue	Paper Title and Paper Interpretation	Code
	KDD Workshop MiLeTS '20	Driver2vec Driver Identification from Automotive Data	Driver2vec
	Adv. Data Anal. Classif. '19	Greedy Gaussian segmentation of multivariate time series 🌟	GGS
	Arxiv '18	MASA: Motif-Aware State Assignment in Noisy Time Series Data	MASA
Ph.D. Thesis	ProQuest '18	Inferring Structure from Multivariate Time Series Sensor Data	None
	KDD '17	Toeplitz Inverse Covariance-Based Clustering of Multivariate Time Series Data 🌟	TICC
	KDD '17	Network Inference via the Time-Varying Graphical Lasso 🌟

Shaghayegh Gharghabi (from Eamonn Keogh's Lab, UC Riverside)

| TYPE | Venue | Paper Title and Paper Interpretation | Code | | :---------------