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2020 – today
- 2024
[c8]Makoto Imamura
, Takaaki Nakamura:
Efficient Discovery of Time Series Motifs under both Length Differences and Warping. KDD 2024: 1188-1198- 2023
- [j1]Takaaki Nakamura, Ryan Mercer, Makoto Imamura, Eamonn J. Keogh:
MERLIN++: parameter-free discovery of time series anomalies. Data Min. Knowl. Discov. 37(2): 670-709 (2023) - [c7]Makoto Imamura, Takaaki Nakamura:
A Symbolic Representation of Two-Dimensional Time Series for Arbitrary Length DTW Motif. ICDM 2023: 1067-1072 - [c6]Yue Lu, Thirumalai Vinjamoor Akhil Srinivas, Takaaki Nakamura, Makoto Imamura, Eamonn J. Keogh:
Matrix Profile XXX: MADRID: A Hyper-Anytime and Parameter-Free Algorithm to Find Time Series Anomalies of all Lengths. ICDM 2023: 1199-1204 - [c5]Makoto Imamura, Takaaki Nakamura:
Parameter-free Spikelet: Discovering Different Length and Warped Time Series Motifs using an Adaptive Time Series Representation. KDD 2023: 857-866 - 2021
- [c4]Makoto Imamura, Takaaki Nakamura:
Spikelet: An Adaptive Symbolic Approximation for Finding Higher-Level Structure in Time Series. ICDM 2021: 1120-1125 - 2020
- [c3]Takaaki Nakamura, Makoto Imamura, Ryan Mercer, Eamonn J. Keogh:
MERLIN: Parameter-Free Discovery of Arbitrary Length Anomalies in Massive Time Series Archives. ICDM 2020: 1190-1195 - [c2]Makoto Imamura, Takaaki Nakamura, Eamonn J. Keogh:
Matrix Profile XXI: A Geometric Approach to Time Series Chains Improves Robustness. KDD 2020: 1114-1122
2000 – 2009
- 2009
- [c1]Tsutomu Sasao, Takaaki Nakamura, Munehiro Matsuura:
Representation of Incompletely Specified Index Generation Functions Using Minimal Number of Compound Variables. DSD 2009: 765-772
Coauthor Index
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