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https://github.com/lzz19980125/awesome-time-series-segmentation-papers
This repository contains a reading list of papers on Time Series Segmentation. This repository is still being continuously improved.
https://github.com/lzz19980125/awesome-time-series-segmentation-papers
List: awesome-time-series-segmentation-papers
anomaly-detection awesome awesome-list change-point-detection data-mining data-science dataset deep-learning machine-learning motion-capture multivariate-time-series paper-list segmentation time-series time-series-analysis time-series-models time-series-segmentation univariate-time-series unsupervised-learning
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This repository contains a reading list of papers on Time Series Segmentation. This repository is still being continuously improved.
- Host: GitHub
- URL: https://github.com/lzz19980125/awesome-time-series-segmentation-papers
- Owner: lzz19980125
- License: gpl-3.0
- Created: 2024-01-27T12:46:19.000Z (9 months ago)
- Default Branch: main
- Last Pushed: 2024-04-28T06:29:27.000Z (6 months ago)
- Last Synced: 2024-05-22T23:01:08.658Z (5 months ago)
- Topics: anomaly-detection, awesome, awesome-list, change-point-detection, data-mining, data-science, dataset, deep-learning, machine-learning, motion-capture, multivariate-time-series, paper-list, segmentation, time-series, time-series-analysis, time-series-models, time-series-segmentation, univariate-time-series, unsupervised-learning
- Language: MATLAB
- Homepage:
- Size: 871 KB
- Stars: 295
- Watchers: 3
- Forks: 4
- Open Issues: 1
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Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Awesome Time Series Segmentation Papers
[](https://awesome.re) 
## Star History
## 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.
## 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 |
| :-----------: | :-------------------------------: | :----------------------------------------------------------: | :----------------------------------------------------------: |
| Dataset | *DARLI-AP@EDBT/ICDT '23* | Time Series Segmentation Applied to a New Data Set for Mobile Sensing of Human Activities 🌟 | [MOSAD](https://github.com/ermshaua/mobile-sensing-human-activity-data-set) |
| Dataset | *ECML-PKDD Workshop '23* | Human Activity Segmentation Challenge@ECML/PKDD’23 🌟 | [Challenge Link](https://ecml-aaltd.github.io/aaltd2023/challenge.html) |
| 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](https://centre-borelli.github.io/ruptures-docs/) |
| Evaluation | *Arxiv '20* | An Evaluation of Change Point Detection Algorithms 🌟 | [TCPDBench](https://github.com/alan-turing-institute/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](http://graphics.cs.cmu.edu/projects/segmentation/) |
## [David Hallac (Stanford)](https://scholar.google.com/citations?hl=zh-CN&user=o23Mn0cAAAAJ&view_op=list_works&sortby=pubdate)
| TYPE | Venue | Paper Title and Paper Interpretation | Code |
| :----------------------------------------------------------: | :----------------------------: | :----------------------------------------------------------: | :----------------------------------------------------------: |
|  | *KDD Workshop MiLeTS '20* | Driver2vec Driver Identification from Automotive Data | [Driver2vec](https://github.com/JingboYang/driver2vec) |
|  | *Adv. Data Anal. Classif. '19* | Greedy Gaussian segmentation of multivariate time series 🌟 | [GGS](https://github.com/cvxgrp/GGS) |
|  | *Arxiv '18* | MASA: Motif-Aware State Assignment in Noisy Time Series Data | [MASA](https://github.com/snap-stanford/masa?utm_source=catalyzex.com) |
| *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](https://github.com/davidhallac/TICC) |
|  | *KDD '17* | Network Inference via the Time-Varying Graphical Lasso 🌟 | [TVGL](https://github.com/davidhallac/TVGL) |
## [Shaghayegh Gharghabi](https://scholar.google.com/citations?hl=en&user=EITBC1YAAAAJ&view_op=list_works&sortby=pubdate) (from [Eamonn Keogh](https://www.cs.ucr.edu/~eamonn/)'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](https://www.cs.ucr.edu/%7eeamonn/FLOSS/)) |
|  | *ICDM '17* | Matrix Profile VIII Domain Agnostic Online Semantic Segmentation at Superhuman Performance Levels 🌟 | [Floss](https://sites.google.com/site/onlinesemanticsegmentation/) |
## [Yasuko Matsubara](https://www.dm.sanken.osaka-u.ac.jp/~yasuko/) & [Yasushi Sakurai](https://www.dm.sanken.osaka-u.ac.jp/~yasushi/) (from [Sakurai & Matsubara Lab](https://www.dm.sanken.osaka-u.ac.jp/))
| TYPE | Venue | Paper Title and Paper Interpretation | Code |
| :----------------------------------------------------------: | :-----------: | :----------------------------------------------------------: | :----------------------------------------------------------: |
|  | *WWW '24* | Dynamic Multi-Network Mining of Tensor Time Series 🌟 | [DMM](https://github.com/KoheiObata/DMM) |
|  | *WWW '23* | Fast and Multi-aspect Mining of Complex Time-stamped Event Streams 🌟 | [CubeScope](https://github.com/kotaNakm/CubeScope) |
|  | *KDD '22* | Fast Mining and Forecasting of Co-evolving Epidemiological Data Streams 🌟 | None |
|  | *CIKM '22* | Modeling Dynamic Interactions over Tensor Streams | [Dismo](https://github.com/kokikwbt/dismo) |
|  | *CIKM '22* | Mining Reaction and Diffusion Dynamics in Social Activities 🌟 | None |
|  | *NeurIPS '21* | SSMF Shifting Seasonal Matrix Factorization | [ssmf](https://github.com/kokikwbt/ssmf) |
|  | *KDD '20* | Non-Linear Mining of Social Activities in Tensor Streams 🌟 | None |
|  | *ICDM '19* | Multi-aspect mining of complex sensor sequences 🌟 | [CubeMarker](https://github.com/TakatoHonda/CubeMarker) |
|  | *KDD '19* | Dynamic Modeling and Forecasting of Time-evolving Data Streams | [OrbitMap](https://github.com/yasuko-matsubara/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](https://www.dm.sanken.osaka-u.ac.jp/~yasuko/SRC/regimecast.zip) |
|  | *WWW '16* | Non-linear mining of competing local activities | [CompCube](https://www.dm.sanken.osaka-u.ac.jp/~yasuko/SRC/compcube.zip) |
|  | *WWW '15* | The web as a jungle: Non-linear dynamical systems for co-evolving online activities 🌟 | [Ecoweb & dataset](https://www.dm.sanken.osaka-u.ac.jp/~yasuko/SRC/ecoweb.zip) |
|  | *SIGMOD '14* | AutoPlait Automatic Mining of Co-evolving Time Sequences 🌟 | [AutoPlait](https://www.dm.sanken.osaka-u.ac.jp/~yasuko/SRC/autoplait.zip) |
|  | *ICDM '14* | Fast and Exact Monitoring of Co-evolving Data Streams | None |
|  | *KDD '14* | FUNNEL Automatic Mining of Spatially Coevolving Epidemics | [Funnel](https://www.dm.sanken.osaka-u.ac.jp/~yasuko/SRC/funnel.zip) |
## [Bryan Hooi](https://bhooi.github.io/) (NUS)
| TYPE | Venue | Paper Title and Paper Interpretation | Code |
| :----------------------------------------------------------: | :------------: | :----------------------------------------------------------: | :----------------------------------------------------------: |
|  | *TKDE '22* | Time Series Anomaly Detection with Adversarial Reconstruction Networks 🌟 | [BeatGAN](https://github.com/BGT-M/spartan2-tutorials/blob/master/BeatGAN.ipynb) |
|  | *IJCAI '19* | BeatGAN Anomalous Rhythm Detection using Adversarially Generated Time Series 🌟 | [BeatGAN](https://github.com/hi-bingo/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](https://bhooi.github.io/code/bnb.zip) |
|  | *SDM '19* | SMF Drift-Aware Matrix Factorization with Seasonal Patterns | [smf & dataset](https://bhooi.github.io/smf/) |
|  | *WWW '17* | AutoCyclone Automatic Mining of Cyclic Online Activities with Robust Tensor Factorization | [AutoCyclone](http://www.cs.cmu.edu/~tsubasat/code/AutoCyclone.zip) |
## [Liangzhe Chen](https://sites.google.com/view/liangzhechen) & [Anika Tabassum](https://scholar.google.com/citations?hl=zh-CN&user=4Aw4_1sAAAAJ&view_op=list_works&sortby=pubdate) (Virginia Tech, supervised by [B. Aditya Prakash](https://scholar.google.com/citations?hl=zh-CN&user=C-NftTgAAAAJ&view_op=list_works&sortby=pubdate))
| 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](https://github.com/AdityaLab/RaTSS) |
|  | *TIST '20* | Cut-n-Reveal: Time-Series Segmentations with Explanations 🌟 | [Cut-n-Reveal](https://github.com/AdityaLab/Cut-n-Reveal) |
|  | *AAAI '18* | Automatic Segmentation of Data Sequences | [DASSA](https://github.com/lzz19980125/awesome-time-series-segmentation-papers/tree/main/DASSA-master) |
| *Ph.D. Thesis* | *ProQuest '18* | Segmenting, Summarizing and Predicting Data Sequences | None |
|  | *vt.edu '18* | Segmentations with Explanations for Outage Analysis 🌟 | None |
## [Shohreh Deldari](https://scholar.google.com/citations?hl=zh-CN&user=250tnREAAAAJ&view_op=list_works&sortby=pubdate) (from [Cruise research group](https://cruiseresearchgroup.github.io/), RMIT ) & [Flora D. Salim](https://scholar.google.com/citations?hl=zh-CN&user=Yz35RSYAAAAJ) (UNSW)
| TYPE | Venue | Paper Title and Paper Interpretation | Code |
| :----------------------------------------------------------: | :--------------------------: | :----------------------------------------------------------: | :----------------------------------------------------------: |
|  | *JAIR '24* | Detecting Change Intervals with Isolation Distributional Kernel 🌟 | [ICD](https://github.com/IsolationKernel/Codes/tree/main/IDK/iCID) |
|  | *IMWUT '22* | COCOA Cross Modality Contrastive Learning for Sensor Data 🌟 | [COCOA](https://github.com/cruiseresearchgroup/COCOA) |
|  | *WWW '21* | Time Series Change Point Detection with Self-Supervised Contrastive Predictive Coding 🌟 | [TSCP2](https://github.com/cruiseresearchgroup/TSCP2) |
|  | *IMWUT '20* | ESPRESSO Entropy and ShaPe awaRe timE-Series SegmentatiOn for Processing Heterogeneous Sensor Data | [ESPRESSO](https://github.com/cruiseresearchgroup/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](https://github.com/cruiseresearchgroup/IGTS-python) |
## [Peng Wang](https://scholar.google.com/citations?hl=en&user=fxcAZkoAAAAJ&view_op=list_works&sortby=pubdate) (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](https://github.com/lzz19980125/awesome-time-series-segmentation-papers/tree/main/GRAB-master) |
|  | *SIGMOD '11* | Finding Semantics in Time Series 🌟 | None |
## [Arik Ermshaus](https://sites.google.com/view/arikermshaus) (Humboldt-Universität zu Berlin)
| TYPE | Venue | Paper Title and Paper Interpretation | Code |
| :----------------------------------------------------------: | :----------------------: | :-------------------------------------------------------: | :----------------------------------------------------------: |
|  | *VLDB '24* | Raising the ClaSS of Streaming Time Series Segmentation 🌟 | [Clasp](https://github.com/ermshaua/classification-score-stream) |
| Dataset | *ECML-PKDD Workshop '23* | Human Activity Segmentation Challenge@ECML/PKDD’23 🌟 | [Challenge Link](https://ecml-aaltd.github.io/aaltd2023/challenge.html) |
|  | *DMKD '23* | ClaSP: parameter-free time series segmentation 🌟 | [Clasp](https://sites.google.com/view/ts-parameter-free-clasp/) |
|  | *CIKM '21* | ClaSP - Time Series Segmentation 🌟 | [Clasp](https://sites.google.com/view/ts-clasp/) |
## [Lei Li](https://lileicc.github.io/) (CMU)
| TYPE | Venue | Paper Title and Paper Interpretation | Code |
| :----------------------------------------------------------: | :------------: | :----------------------------------------------------------: | :----------------------------------------------------------: |
|  | *Neurips '13* | MLDS Multilinear Dynamical Systems for Tensor Time Series | [mlds](https://github.com/lileicc/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](https://github.com/lileicc/dynammo) |
|  | *VLDB '10* | Parsimonious Linear Fingerprinting for Time Series | [pliF](https://lileicc.github.io/software/plif-r345.zip) |
## [Feng Zhou](https://www.f-zhou.com/) (CMU)
| TYPE | Venue | Paper Title and Paper Interpretation | Code |
| :----------------------------------------------------------: | :---------: | :----------------------------------------------------------: | :------------------------------------: |
|  | *TPAMI '12* | Hierarchical Aligned Cluster Analysis for Temporal Clustering of Human Motion 🌟 | [HACA](https://www.f-zhou.com/tc.html) |
## [Chun-Tung Li](https://scholar.google.com/citations?hl=en&user=9qsSJ-0AAAAJ&view_op=list_works&sortby=pubdate) (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](https://github.com/chuntungli/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](https://scholar.google.com/citations?hl=en&user=RaINcuUAAAAJ&view_op=list_works&sortby=pubdate) (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](https://github.com/baoyujing/NET3) |
|  | *SDM '15* | Fast Mining of a Network of Coevolving Time Series | [dcmf](https://github.com/kokikwbt/dcmf/tree/master) (Unofficial)  |
|  | *KDD '15* | Facets: Fast comprehensive mining of coevolving high-order time | [facets](https://github.com/kokikwbt/facets) (Unofficial)  |
## Others
| TYPE | Venue | Paper Title and Paper Interpretation | Code |
| :----------------------------------------------------------: | :---------------------------: | :----------------------------------------------------------: | :----------------------------------------------------------: |
|  | *SDM'24* | Pattern-based Time Series Semantic Segmentation with Gradual State Transitions | [Patss](https://gitlab.kuleuven.be/u0143709/patss) [Dataset](https://rdr.kuleuven.be/dataset.xhtml?persistentId=doi:10.48804/G2YRDR) |
|  | *TKDE'24* | Discovering Dynamic Patterns From Spatiotemporal Data With Time-Varying Low-Rank Autoregression | [Vars](https://github.com/xinychen/vars) |
|  | *WWW '24* | E2Usd: Efficient-yet-effective Unsupervised State Detection for Multivariate Time Series 🌟 | [E2Usd](https://github.com/AI4CTS/E2Usd) |
|  | *Information Fusion '24* | MultiBEATS Blocks of eigenvalues algorithm for multivariate time series dimensionality reduction 🌟 | [MultiBEATS](https://github.com/auroragonzalez/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](https://github.com/caozhenxiang/MC-TIRE) |
|  | *TII '23* | A Boundary Consistency-Aware Multitask Learning Framework for Joint Activity Segmentation and Recognition With Wearable Sensors | [Coming soom](https://github.com/xspc/Segmentation-Sensor-based-HAR) 🙃 |
|  | *SIGMOD '23* | Time2State: An Unsupervised Framework for Inferring the Latent States in Time Series Data 🌟 | [Time2State](https://github.com/Lab-ANT/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 | None |
|  | *JASA'22* | Factor Models for High-Dimensional Tensor Time Series | None |
|  | *JSS'22* | Analysis of Tensor Time Series: tensorTS | [tensorTS](https://github.com/ZeBang/tensorTS) |
|  | *IMWUT '22* | ColloSSL Collaborative Self-Supervised Learning for Human Activity Recognition 🌟 | [collossl](https://github.com/akhilmathurs/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](https://doi.org/10.1080/00401706.2021.1927848) |
|  | *Neurips Workshop '22* | Are uGLAD? Time will tell! 🌟 | [tGLAD](https://github.com/Harshs27/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](https://sleep.ai.ku.dk/) |
|  | *IEEE TSP '21* | Change Point Detection in Time Series Data Using Autoencoders With a Time-Invariant Representation 🌟 | [TIRE](https://github.com/deryckt/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](https://github.com/ChunjingXiao/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](https://goo.gl/TV1seZ) |
|  | *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](https://github.com/hlim-kist/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](https://github.com/perslev/U-Time) |
|  | *Neurocomputing '19* | A hybrid dynamic exploitation barebones particle swarm optimisation algorithm for time series segmentation | [tssa](https://github.com/ayrna/tssa?tab=readme-ov-file) |
|  | *TKDE '18* | BEATS Blocks of Eigenvalues Algorithm for Time series Segmentation 🌟 | [BEATS](https://github.com/auroragonzalez/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](https://github.com/AjeyPaiK/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](https://github.com/anewgithubname/change_detection?tab=readme-ov-file)  |