Paper Info
Title
Online data mining for co-evolving time sequences
Abstract
In many applications, the data of interest comprises multiple sequences that evolve over time. Examples include currency exchange rates and network traffic data. We develop a fast method to analyze such co-evolving time sequences jointly to allow (a) estimation/forecasting of missing/delayed/future values, (b) quantitative data mining, and (c) outlier detection. Our method, MUSCLES, adapts to changing correlations among time sequences. It can handle indefinitely long sequences efficiently using an incremental algorithm and requires only a small amount of storage and less I/O operations. To make it scale for a large number of sequences, we present a variation, the Selective MUSCLES method and propose an efficient algorithm to reduce the problem size. Experiments on real datasets show that MUSCLES outperforms popular competitors in prediction accuracy up to 10 times, and discovers interesting correlations. Moreover, Selective MUSCLES scales up very well for large numbers of sequences, reducing response time up to 110 times over MUSCLES, and sometimes even improves the prediction quality
Year
DOI
Venue
2000
10.1109/ICDE.2000.839383
ICDE
Keywords
Field
DocType
incremental algorithm,response time,prediction accuracy,particular focus,value forecasting,co-evolving time sequences,changing correlations,ratio law,datasets,sequences,online data mining,prediction quality,value estimation,muscles method,data mining,quantitative data mining,data storage system,i/o operations,selective muscles method,outlier detection,networking cost,demography,internet,data bases,information retrieval,tellurium,systems analysis,algorithms,least squares method,data management,information transfer,regression analysis
Least squares,Data mining,Anomaly detection,Information transfer,Computer science,Regression analysis,Systems analysis,Response time,Data management,Electrical capacitance tomography,Database
Conference
ISSN
ISBN
Citations 
1063-6382
0-7695-0506-6
97
PageRank 
References 
Authors
9.96
11
6
Name
Order
Citations
PageRank
byoungkee yi1979.96
N. D. Sidiropoulos293069.06
theodore johnson3979.96
H. V. Jagadish4111412495.67
Christos Faloutsos5279724490.38
Alexandros Biliris6407197.90