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Extended vertical lists for temporal pattern mining from multivariate time series
Author(s) -
Kocheturov Anton,
Momcilovic Petar,
Bihorac Azra,
Pardalos Panos M.
Publication year - 2019
Publication title -
expert systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.365
H-Index - 38
eISSN - 1468-0394
pISSN - 0266-4720
DOI - 10.1111/exsy.12448
Subject(s) - computer science , prefix , extension (predicate logic) , multivariate statistics , series (stratigraphy) , data mining , context (archaeology) , property (philosophy) , state (computer science) , time series , algorithm , pattern recognition (psychology) , artificial intelligence , machine learning , geology , paleontology , philosophy , linguistics , epistemology , programming language
In this paper, the problem of mining complex temporal patterns in the context of multivariate time series is considered. A new method called the Fast Temporal Pattern Mining with Extended Vertical Lists is introduced. The method is based on an extension of the level‐wise property, which requires a more complex pattern to start at positions within a record where all of the subpatterns of the pattern start. The approach is built around a novel data structure called the Extended Vertical List that tracks positions of the first state of the pattern inside records and links them to appropriate positions of a specific subpattern of the pattern called the prefix. Extensive computational results indicate that the new method performs significantly faster than the previous version of the algorithm for Temporal Pattern Mining; however, the increase in speed comes at the expense of increased memory usage.