Indranil R. Bardhan, Sugato Bagchi, et al.
JMIS
This work motivates the need for more flexible structural similarity measures between time-series sequences, which are based on the extraction of important periodic features. Specifically, we present non-parametric methods for accurate periodicity detection and we introduce new periodic distance measures for time-series sequences. We combine these new measures with an effective metric tree index structure for efficiently answering k-Nearest-Neighbor queries. The goal of these tools and techniques are to assist in detecting, monitoring and visualizing structural periodic changes. It is our belief that these methods can be directly applicable in the manufacturing industry for preventive maintenance and in the medical sciences for accurate classification and anomaly detection. © 2005 Springer Science+Business Media, Inc.
Indranil R. Bardhan, Sugato Bagchi, et al.
JMIS
Hans Becker, Frank Schmidt, et al.
Photomask and Next-Generation Lithography Mask Technology 2004
B. Wagle
EJOR
Yigal Hoffner, Simon Field, et al.
EDOC 2004