Please use this identifier to cite or link to this item:
|Title:||Lag patterns in time series databases||Authors:||Patel, D.
|Issue Date:||2010||Citation:||Patel, D.,Hsu, W.,Lee, M.L.,Parthasarathy, S. (2010). Lag patterns in time series databases. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 6262 LNCS (PART 2) : 209-224. ScholarBank@NUS Repository. https://doi.org/10.1007/978-3-642-15251-1_17||Abstract:||Time series motif discovery is important as the discovered motifs generally form the primitives for many data mining tasks. In this work, we examine the problem of discovering groups of motifs from different time series that exhibit some lag relationships. We define a new class of pattern called lagPatterns that captures the invariant ordering among motifs. lagPatterns characterize localized associative pattern involving motifs derived from each entity and explicitly accounts for lag across multiple entities. We present an exact algorithm that makes use of the order line concept and the subsequence matching property of the normalized time series to find all motifs of various lengths. We also describe a method called LPMiner to discover lagPatterns efficiently. LPMiner utilizes inverted index and motif alignment technique to reduce the search space and improve the efficiency. A detailed empirical study on synthetic datasets shows the scalability of the proposed approach. We show the usefulness of lagPatterns discovered from a stock dataset by constructing stock portfolio that leads to a higher cumulative rate of return on investment. © 2010 Springer-Verlag.||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/10635/40943||ISBN:||3642152503||ISSN:||03029743||DOI:||10.1007/978-3-642-15251-1_17|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Apr 13, 2019
checked on Dec 29, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.