awesome-subsequence-outlier

general

• [ACM CSUR 2018] Spatio-temporal data mining: A survey of problems and methods
• [IJIM 2019] Real-time big data processing for anomaly detection: A survey

anomalous trajectories

survey

• [Artificial Intelligence Review 2018] An overview on trajectory outlier detection
• [ACM TMIS 2020] Trajectory Outlier Detection: Algorithms, Taxonomies, Evaluation, and Open Challenges

specific

anomaloy detection

• [KDD 2009] Efficient anomaly monitoring over moving object trajectory streams
• [CIKM 2010] TOP-EYE: Top-k evolving trajectory outlier detection
• [UbiComp 2011] iBAT: Detecting anomalous taxi trajectories from GPS traces
• [KDD 2016] Mantra: A scalable approach to mining temporally anomalous sub-trajectories
• [TITS 2013] iBOAT: Isolation-based online anomalous trajectory detection
• [MNA 2013] Real time anomalous trajectory detection and analysis (extension of iBOAT)
• [TKDD 2014] Anomaly detection from incomplete data (BT-miner)
• [PMC 2015] Disorientation detection by mining GPS trajectories for cognitively-impaired elders (iBDD)
• [ICPR 2016] Granular trajectory based anomaly detection for surveillance (ROSE)
• [TODS 2017] Outlier detection over massive-scale trajectory streams (TN-outlier)
• [PAKDD 2018] Sub-trajectory- and Trajectory-Neighbor-based Outlier Detection over Trajectory Streams
• [SDM 2019] Outlier Detection over Distributed Trajectory Streams
• [TIST 2021] Feature Grouping–based Trajectory Outlier Detection over Distributed Streams

subtrajectory clustering

• [PODS 2018] Subtrajectory Clustering: Models and Algorithms
• [Big Data 2019] Scalable distributed subtrajectory clustering

datasets

• GPS-UCI 603 trajectories with 5,317 different points; the number of points per trajectory exceeds 2,000 (sparse)
• Geolife 17,621 trajectories with 152,241 different points; the number of points per trajectory exceeds 5,000
• Manhattan 1,000 taxi trajectories collected over a 1-year period; the number of points per trajectory exceeds 1,000 (sparse)
• Geomesa (1) taxi 13-1 containing 1.89 million trajectories, (2) taxi 13-2 containing 3.69 million trajectories, and (3) taxi 15 containing 57,000 trajectories; each trajectory contains more than 1,500 different points (sparse)

evaluation metrics

usually ground truth is synthesized; random noise-injection approaches.

F-measures and AUC (accuracy of finding outlier (sub-)trajectories from inlier ones)

anomalous time-series

survey

• [TKDE 2010] Anomaly detection for discrete sequences: A survey
• [IJCA 2012] Recent Techniques of Clustering of Time Series Data: A Survey
• [Information Systems 2015] Time-series clustering – A decade review

specifics

anomaloy detection

• [ICDE 2019] Automated Anomaly Detection in Large Sequences (SAD)
• [VLDB 2020] Series2Graph: Graph-based Subsequence Anomaly Detection for Time Series
• [VLDB 2021] SAND: Streaming Subsequence Anomaly Detection

subsequence clustering

• [SIGMOD 2015] k-Shape: Efficient and Accurate Clustering of Time Series