Awesome Time Series Segmentation Papers
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
🚩 2024/1/27: I have marked some recommended papers / datasets / implementations with 🌟 (Just my personal preference 😉).
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
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
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
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
TYPE
Venue
Paper Title and Paper Interpretation
Code
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
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
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
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
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
TYPE
Venue
Paper Title and Paper Interpretation
Code
Arxiv '23
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
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
TYPE
Venue
Paper Title and Paper Interpretation
Code
TPAMI '12
Hierarchical Aligned Cluster Analysis for Temporal Clustering of Human Motion 🌟
HACA
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
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)
TYPE
Venue
Paper Title and Paper Interpretation
Code
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
None
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