Please use this identifier to cite or link to this item: https://doi.org/10.1007/978-3-642-45269-7_4
Title: Efficient mining of lag patterns in evolving time series
Authors: Patel, D.
Hsu, W. 
Lee, M.L. 
Keywords: Incremental mining
Pattern discovery
Time series motif
Issue Date: 2013
Source: Patel, D.,Hsu, W.,Lee, M.L. (2013). Efficient mining of lag patterns in evolving time series. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 8290 LNCS : 76-101. ScholarBank@NUS Repository. https://doi.org/10.1007/978-3-642-45269-7_4
Abstract: Time series motifs are sets of similar subsequences. Lag patterns, or the invariant ordering among time series motifs, depict localized repeated associative relationships across multiple real valued time series. Lag patterns are of special interest in many real world applications, such as constructing stock portfolio in financial domain, extracting regulator-target relationship in bioinformatics domain, etc. However, mining lag patterns is computationally intensive, particularly in evolving time series data. In this paper, we present an efficient algorithm called LPMiner? that iteratively discovers motifs and generates lag patterns of increasing length. We also design an incremental algorithm called incLPMiner to mine lag patterns in the presence of frequent database updates. Experimental analysis on real world time series datasets demonstrate the efficiency and scalability of our proposed algorithms. © Springer-Verlag Berlin Heidelberg 2013.
Source Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
URI: http://scholarbank.nus.edu.sg/handle/10635/77848
ISBN: 9783642452680
ISSN: 03029743
DOI: 10.1007/978-3-642-45269-7_4
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

Page view(s)

33
checked on Apr 20, 2018

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.