今村 誠

Matrix Profile XXX: MADRID: A Hyper-Anytime and Parameter-Free Algorithm to Find Time Series Anomalies of all Lengths. 

ICDM 1199-1204 2023年  

A Symbolic Representation of Two-Dimensional Time Series for Arbitrary Length DTW Motif. 

ICDM 1067-1072 2023年  

MERLIN++: parameter-free discovery of time series anomalies 

Data Mining and Knowledge Discovery 2023年  

Spikelet: An Adaptive Symbolic Approximation for Finding Higher-Level Structure in Time Series. 

ICDM 1120-1125 2021年  査読有り筆頭著者

MERLIN: Parameter-free discovery of arbitrary length anomalies in massive time series archives 

Proceedings - IEEE International Conference on Data Mining, ICDM 2020-November 1190-1195 2020年11月  

Matrix Profile XXI: A Geometric Approach to Time Series Chains Improves Robustness 

Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 1114-1122 2020年8月23日  査読有り筆頭著者

Introducing time series chains: a new primitive for time series data mining 

Knowledge and Information Systems 60(2) 1135-1161 2019年8月1日  

Time series chains: A novel tool for time series data mining 

IJCAI International Joint Conference on Artificial Intelligence 2018-July 5414-5418 2018年  

Matrix profile VII: Time series chains: A new primitive for time series data mining (best student paper award) 

Proceedings - IEEE International Conference on Data Mining, ICDM 2017-November 695-704 2017年12月15日  

Exemplar learning for extremely efficient anomaly detection in real-valued time series 

Data Mining and Knowledge Discovery 30(6) 1427-1454 2016年11月1日