awesome-time-series-segmentation-papers

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:

Zezhong Li, Ling Wang (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.

Zezhong Li, Wei Guo, Jianpeng An, Qi Wang, Yingchun Mei, Rongshun Juan, Tianshu Wang, Yang Li, and Zhongke Gao, AD2T: Multivariate Time Series Anomaly Detection with Association Discrepancy Dual-Decoder Transformer, IEEE Sensors Journal (IEEE Sens J), 2025. https://ieeexplore.ieee.org/document/10906418.

Ling Wang, Qinchun Xu, Zezhong Li, STANAP: Spatiotemporal Adaptive Neighborhood-Aware Prediction with Multimodal Evolution, Knowledge-Based Systems (KBS), 2026. https://doi.org/10.1016/j.knosys.2026.116346.

@article{LI2026JT3D,
  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}
}

@ARTICLE{Li2025AD2T,
  author={Li, Zezhong and Guo, Wei and An, Jianpeng and Wang, Qi and Mei, Yingchun and Juan, Rongshun and Wang, Tianshu and Li, Yang and Gao, Zhongke},
  journal={IEEE Sensors Journal}, 
  title={AD2T: Multivariate Time-Series Anomaly Detection With Association Discrepancy Dual-Decoder Transformer}, 
  year={2025},
  volume={25},
  number={7},
  pages={11710-11721},
  keywords={Anomaly detection;Sensors;Transformers;Training;Time series analysis;Long short term memory;Correlation;Servers;Sensor phenomena and characterization;Measurement;Anomaly detection;association discrepancy;dual-decoder;multivariate time series (MTS);transformer},
doi={10.1109/JSEN.2025.3543835}}

@article{WANG2026STANAP,
title = {STANAP: Spatiotemporal adaptive neighborhood-aware prediction with multimodal evolution},
journal = {Knowledge-Based Systems},
volume = {348},
pages = {116346},
year = {2026},
issn = {0950-7051},
doi = {https://doi.org/10.1016/j.knosys.2026.116346},
url = {https://www.sciencedirect.com/science/article/pii/S0950705126010725},
author = {Ling Wang and Qinchun Xu and Zezhong Li},
}

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 🌟	Ruptures
Evaluation	Arxiv '20	An Evaluation of Change Point Detection Algorithms 🌟	TCPDBench
Survey	Knowl. Inf. Syst. '17	A survey of methods for time series change point detection 🌟	None
Evaluation	Inf. Syst. '17	An evaluation of combinations of lossy compression and change-detection approaches for time-series data	None
Survey	IEEE Trans Hum. Mach. Syst. '16	Movement Primitive Segmentation for Human Motion Modeling A Framework for Analysis 🌟	None
Survey	EAAI '11	A review on time series data mining	None
Survey	CSUR '11	Time-series data mining	None
Dataset	GI '04	Segmenting Motion Capture Data into Distinct Behaviors 🌟	Website

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 🌟	TVGL

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

TYPE	Venue	Paper Title and Paper Interpretation	Code
	DMKD '19	Domain agnostic online semantic segmentation for multi-dimensional time series 🌟	Floss & datasets)
	ICDM '17	Matrix Profile VIII Domain Agnostic Online Semantic Segmentation at Superhuman Performance Levels 🌟	Floss

Yasuko Matsubara & Yasushi Sakurai (from Sakurai & Matsubara Lab)

TYPE	Venue	Paper Title and Paper Interpretation	Code
	KDD '25	MicroAdapt Self-Evolutionary Dynamic Modeling Algorithms for Time-evolving Data Streams	microadapt
	WWW '25 short	CyberCScope: Mining Skewed Tensor Streams and Online Anomaly Detection in Cybersecurity Systems 🌟	CyberCScope
	KDD '25	D-Tracker Modeling Interest Diffusion in Social Activity Tensor Data Streams	D-Tracker
	KDD '24	Mining of Switching Sparse Networks for Missing Value Imputation in Multivariate Time Series 🌟	MissNet
	WWW '24	Dynamic Multi-Network Mining of Tensor Time Series 🌟	DMM
	WWW '23	Fast and Multi-aspect Mining of Complex Time-stamped Event Streams 🌟	CubeScope
	KDD '22	Fast Mining and Forecasting of Co-evolving Epidemiological Data Streams 🌟	None
	CIKM '22	Modeling Dynamic Interactions over Tensor Streams	Dismo
	CIKM '22	Mining Reaction and Diffusion Dynamics in Social Activities 🌟	None
	NeurIPS '21	SSMF Shifting Seasonal Matrix Factorization	ssmf
	KDD '20	Non-Linear Mining of Social Activities in Tensor Streams 🌟	None
	ICDM '19	Multi-aspect mining of complex sensor sequences 🌟	CubeMarker
	KDD '19	Dynamic Modeling and Forecasting of Time-evolving Data Streams	OrbitMap
	CIKM '19	Automatic Sequential Pattern Mining in Data Streams	None
	KDD '16	Regime Shifts in Streams: Real-time Forecasting of Co-evolving Time Sequences	RegimeCast
	WWW '16	Non-linear mining of competing local activities	CompCube
	WWW '15	The web as a jungle: Non-linear dynamical systems for co-evolving online activities 🌟	Ecoweb & dataset
	SIGMOD '14	AutoPlait Automatic Mining of Co-evolving Time Sequences 🌟	AutoPlait
	ICDM '14	Fast and Exact Monitoring of Co-evolving Data Streams	None
	KDD '14	FUNNEL Automatic Mining of Spatially Coevolving Epidemics	Funnel

Bryan Hooi (NUS)

TYPE	Venue	Paper Title and Paper Interpretation	Code
	TKDE '22	Time Series Anomaly Detection with Adversarial Reconstruction Networks 🌟	BeatGAN
	IJCAI '19	BeatGAN Anomalous Rhythm Detection using Adversarially Generated Time Series 🌟	BeatGAN
Ph.D. Thesis	ProQuest '19	Anomaly Detection in Graphs and Time Series Algorithms and Applications	None
	SDM '19	Branch and Border Partition Based Change Detection in Multivariate Time Series 🌟	Bnb
	SDM '19	SMF Drift-Aware Matrix Factorization with Seasonal Patterns	smf & dataset
	WWW '17	AutoCyclone Automatic Mining of Cyclic Online Activities with Robust Tensor Factorization	AutoCyclone

Liangzhe Chen & Anika Tabassum (Virginia Tech, supervised by B. Aditya Prakash)

TYPE	Venue	Paper Title and Paper Interpretation	Code
Ph.D. Thesis	ProQuest '21	Explainable and Network-Based Approaches for Decision-making in Emergency Management	None
	CIKM '21	Actionable Insights in Urban Multivariate Time-series	RaTSS
	TIST '20	Cut-n-Reveal: Time-Series Segmentations with Explanations 🌟	Cut-n-Reveal
	AAAI '18	Automatic Segmentation of Data Sequences	DASSA
Ph.D. Thesis	ProQuest '18	Segmenting, Summarizing and Predicting Data Sequences	None
	vt.edu '18	Segmentations with Explanations for Outage Analysis 🌟	None

Shohreh Deldari (from Cruise research group, RMIT ) & Flora D. Salim (UNSW)

TYPE	Venue	Paper Title and Paper Interpretation	Code
	JAIR '24	Detecting Change Intervals with Isolation Distributional Kernel 🌟	ICD
	IMWUT '22	COCOA Cross Modality Contrastive Learning for Sensor Data 🌟	COCOA
	WWW '21	Time Series Change Point Detection with Self-Supervised Contrastive Predictive Coding 🌟	TSCP2
	IMWUT '20	ESPRESSO Entropy and ShaPe awaRe timE-Series SegmentatiOn for Processing Heterogeneous Sensor Data	ESPRESSO
	Knowl. Inf. Syst. '20	Unsupervised online change point detection in high-dimensional time series	None
	WSDM Workshop '19	Inferring Work Routines and Behavior Deviations with Life-logging Sensor Data	None
	Pervasive Mob. Comput. '17	Information gain-based metric for recognizing transitions in human activities 🌟	IGTs

Peng Wang (fudan University)

TYPE	Venue	Paper Title and Paper Interpretation	Code
	ICDE '21	GRAB: Finding Time Series Natural Structures via A Novel Graph-based Scheme	GRAB
	SIGMOD '11	Finding Semantics in Time Series 🌟	None

Arik Ermshaus (Humboldt-Universität zu Berlin)

TYPE	Venue	Paper Title and Paper Interpretation	Code
	Arxiv '25	CLaP - State Detection from Time Series	cLaP
	AALTD '25	Multivariate Human Activity Segmentation: Systematic Benchmark with ClaSP	multivariate-clasp
	VLDB '24	Raising the ClaSS of Streaming Time Series Segmentation 🌟	Clasp
Dataset	ECML-PKDD Workshop '23	Human Activity Segmentation Challenge@ECML/PKDD’23 🌟	Challenge Link
	DMKD '23	ClaSP: parameter-free time series segmentation 🌟	Clasp
	CIKM '21	ClaSP - Time Series Segmentation 🌟	Clasp

Lei Li (CMU)

TYPE	Venue	Paper Title and Paper Interpretation	Code
	Neurips '13	MLDS Multilinear Dynamical Systems for Tensor Time Series	mlds
Ph.D. Thesis	ProQuest '11	Fast Algorithms for Mining Co-evolving Time Series	None
	KDD '09	DynaMMo: Mining and Summarization of Coevolving Sequences with Missing Values 🌟	dynammo
	VLDB '10	Parsimonious Linear Fingerprinting for Time Series	pliF

Feng Zhou (CMU)

TYPE	Venue	Paper Title and Paper Interpretation	Code
	TPAMI '12	Hierarchical Aligned Cluster Analysis for Temporal Clustering of Human Motion 🌟	HACA

Chun-Tung Li (CUHK)

TYPE	Venue	Paper Title and Paper Interpretation	Code
	ACM Trans. Comput. Healthcare '20	mSIMPAD: Efficient and Robust Mining of Successive Similar Patterns of Multiple Lengths in Time Series 🌟	mSIMPAD
Ph.D. Thesis	ProQuest '21	Mobile sensing based human stress monitoring for smart health applications	None
	IEEE MASS '21	Repetitive Activity Monitoring from Multivariate Time Series A Generic and Efficient Approach	None

Tong Hanghang (UIUC)

TYPE	Venue	Paper Title and Paper Interpretation	Code
	Arxiv'24	Tensor time-series forecasting and anomaly detection with augmented causality	None
	WWW'21	Network of Tensor Time Series	NET3
	SDM '15	Fast Mining of a Network of Coevolving Time Series	dcmf (Unofficial)
	KDD '15	Facets: Fast comprehensive mining of coevolving high-order time	facets (Unofficial)

Others

TYPE	Venue	Paper Title and Paper Interpretation	Code
	ECML PKDD '26	tsseg: An Interactive Toolkit for Time Series Segmentation	tsseg
	NEUCOM '26	JT3D: Joint Temporal Segmentation for Common Pattern Discovery in 3D Tensor Time Series via Segmented Gaussian Modeling and Information-Gain	None
	NeurIPS '25	Toward Interpretable Evaluation Measures for Time Series Segmentation	Seg-Eval
	VLDB Ph.D. Workshop '25	Toward Interpretable Methods for Time Series Analytics	None
	SIGMOD '25	ISSD: Indicator Selection for Time Series State Detection	ISSD
	ICML '25 Workshop	Do Large Foundation Models Improve Time Series Segmentation An Industrial Case Study in Oil and Gas Drilling	None
	Sci. Adv. '25	Deep manifold learning reveals hidden developmental dynamics of a human embryo model	code, dataset
	CIKM '25	PromptTSS A Prompting-Based Approach for Interactive Multi-Granularity Time Series Segmentation	PromptTSS
	TKDE '25	B-CAVE: A Robust Online Time Series Change Point Detection Algorithm Based on the Between-Class Average and Variance Evaluation Approach	B-CAVE
	Information Fusion '25	Identifying the temporal distribution structure in multivariate data for time-series segmentation based on two-sample test	MIDAST
	IEEE/CAA Journal of Automatica Sinica '25	Multi-Scale Time Series Segmentation Network Based on Eddy Current Testing for Detecting Surface Metal Defects	MSSN
	CEP '25	Enhanced variational autoencoder with continual learning capability for multimode process monitoring	None
	IEEE IOTJ '25	Multidimensional Time Series Segmentation of Human Activity Without Prior Knowledge	None
	IEEE TIM '25	Unsupervised Fault Detection Method via Time-Series Segmentation and Contrastive Masking Learning	None
	MSSP '25	An evolutionary vehicle scanning method for bridges based on time series segmentation and change point detection	None
	OE '25	SteadySeg Improving maritime trajectory staging by steadiness recognition	None
	Applied Energy '25	Is it returning too hot Time series segmentation and feature clustering of end-user substation faults in district heating systems	code
	AISTATS '24	Unsupervised Change Point Detection in Multivariate Time Series	None
	ACM AAIA '24	Detecting State Correlations between Heterogeneous Time Series	None
	KNOWL INF SYST. (KAIS) '24	Optimal online time-series segmentation	OSFS
	AEI '24	SIMTSeg: A self-supervised multivariate time series segmentation method with periodic subspace projection and reverse diffusion for industrial process	None
	Def Technol. '24	An air combat maneuver pattern extraction based on time series segmentation and clustering analysis	None
	Phys. A '24	An adaptive time series segmentation algorithm based on visibility graph and particle swarm optimization	VG-APSO
	CIKM'24	Towards Uncertainty Quantification for Time Series Segmentation	UQ-TSS
	SDM'24	Pattern-based Time Series Semantic Segmentation with Gradual State Transitions	Patss Dataset
	TKDE'24	Discovering Dynamic Patterns From Spatiotemporal Data With Time-Varying Low-Rank Autoregression	Vars
	WWW '24	E2Usd: Efficient-yet-effective Unsupervised State Detection for Multivariate Time Series 🌟	E2Usd
	Information Fusion '24	MultiBEATS Blocks of eigenvalues algorithm for multivariate time series dimensionality reduction 🌟	MultiBEATS
	Information Sciences '24	Memetic segmentation based on variable lag aware for multivariate time series 🌟	None
	TKDE '23	Change Point Detection in Multi-channel Time Series via a Time-invariant Representation 🌟	MC-TIRE
	TII '23	A Boundary Consistency-Aware Multitask Learning Framework for Joint Activity Segmentation and Recognition With Wearable Sensors	Coming soom 🙃
	SIGMOD '23	Time2State: An Unsupervised Framework for Inferring the Latent States in Time Series Data 🌟	Time2State
	TKDD '23	Modeling Regime Shifts in Multiple Time Series	None
	World Wide Web '23	Anomaly and change point detection for time series with concept drift	None
	EAAI '23	PrecTime A deep learning architecture for precise time series segmentation in industrial manufacturing operations	Dataset
	JASA'22	Factor Models for High-Dimensional Tensor Time Series	None
	JSS'22	Analysis of Tensor Time Series: tensorTS	tensorTS
	IMWUT '22	ColloSSL Collaborative Self-Supervised Learning for Human Activity Recognition 🌟	collossl
	MSSP '22	A multivariate time series segmentation algorithm for analyzing the operating statuses of tunnel boring machines	None
	Technometrics '22	Bayesian Hierarchical Model for Change Point Detection in Multivariate Sequences	Supplementary Materials
	Neurips Workshop '22	Are uGLAD? Time will tell! 🌟	tGLAD
	Applied Intelligence '22	Change point detection for compositional multivariate data	None
	ICDM '22	Change Detection with Probabilistic Models on Persistence Diagrams	None
	EAAI '22	Graft : A graph based time series data mining framework	None
	GLOBECOM '22	Multi-level Contrast Network for Wearables-based Joint Activity Segmentation and Recognition	None
	ESWA '22	Real-time Change-Point Detection A deep neural network-based adaptive approach for detecting changes in multivariate time series data	None
	npj digital medicine '21	U-Sleep: resilient high-frequency sleep staging 🌟	website
	IEEE TSP '21	Change Point Detection in Time Series Data Using Autoencoders With a Time-Invariant Representation 🌟	TIRE
	IJCNN '21	A Transferable Technique for Detecting and Localising Segments of Repeating Patterns in Time series	None
	IOTJ '21	DeepSeg Deep-Learning-Based Activity Segmentation Framework for Activity Recognition Using WiFi	DeepSeg
	Information Sciences '21	Change-point detection based on adjusted shape context method cost	None
	KDD '21	Statistical Models Coupling Allows for Complex Local Multivariate Time Series Analysis	None
	IEEE TCYB '20	An Online Unsupervised Dynamic Window Method to Track Repeating Patterns From Sensor Data 🌟	FingdingIOR
	Pattern Recognit. Lett. '20	A new approach for optimal time-series segmentation	None
	SDM '20	Lag-aware multivariate time-series segmentation 🌟	None
	Pattern Recognit. Lett. '20	Memetic algorithm for multivariate time-series segmentation 🌟	ma_mts
	ICASSP '20	Modeling Piece-Wise Stationary Time Series	None
	Neurips '19	U-Time: A Fully Convolutional Network for Time Series Segmentation Applied to Sleep Staging 🌟	U-Time
	Neurocomputing '19	A hybrid dynamic exploitation barebones particle swarm optimisation algorithm for time series segmentation	tssa
	TKDE '18	BEATS Blocks of Eigenvalues Algorithm for Time series Segmentation 🌟	BEATS
	Arxiv '18	Time Series Segmentation through Automatic Feature Learning 🌟	None
	Applied Soft Computing '16	Change points detection in crime-related time series An on-line fuzzy approach based on a shape space representation	None
	WACV '16	Decomposing Time Series with application to Temporal Segmentation 🌟	Hog1D (Unofficial)
	J. Am. Stat. Assoc. '14	A Nonparametric Approach for Multiple Change Point Analysis of Multivariate Data	None
	Neural Networks '13	Change-point detection in time-series data by relative density-ratio estimation 🌟	RuLSIF
	Frontiers in Neurorobotics Volume 11 - 2017	Segmenting Continuous Motions with Hidden Semi-markov Models and Gaussian Processes	GP-HSMM