Paper Info
Title
Fast correlation analysis on time series datasets
Abstract
There has been increasing interest for efficient techniques for fast correlation analysis of time series data in different application domains. We present three algorithms for (1) bivariate correlation queries, (2) multivariate correlation queries, and (3) correlation queries based on a new correlation measure we introduce using dynamic time warping. To support these algorithms, we use a variant of the Compact Multi-Resolution Index (CMRI). In addition to conventional nearest neighbor and range queries supported by CMRI, the proposed algorithms compute all answers to user-defined, ad hoc and parametric correlation queries. The results of our experiments indicate a speed-up of two orders of magnitude over the brute force algorithm, and an order of magnitude improvement on average, while offering more functionalities than provided by existing techniques such as StatStream and the Spatial Cone Tree.
Year
DOI
Venue
2008
10.1145/1458082.1458187
CIKM
Keywords
Field
DocType
magnitude improvement,bivariate correlation query,parametric correlation query,dynamic time warping,new correlation measure,compact multi-resolution index,time series datasets,spatial cone tree,fast correlation analysis,time series data,multivariate correlation query,nearest neighbor,time series,indexation,range query
k-nearest neighbors algorithm,Time series,Data mining,Multiple correlation,Brute-force search,Pattern recognition,Dynamic time warping,Computer science,Range query (data structures),Parametric statistics,Artificial intelligence,Bivariate analysis
Conference
Citations 
PageRank 
References 
3
0.38
19
Authors
2
Name
Order
Citations
PageRank
Philon Nguyen131.73
Nematollaah Shiri228028.31