Paper Info
Title
Empirical Studies On Symbolic Aggregation Approximation Under Statistical Perspectives For Knowledge Discovery In Time Series
Abstract
Symbolic Aggregation approXimation (SAX) has been the de facto standard representation methods for knowledge discovery in time series on a number of tasks and applications. So far, very little work has been done in empirically investigating the intrinsic properties and statistical mechanics in SAX words. In this paper, we applied several statistical measurements and proposed a new statistical measurement, i.e. information embedding cost (IEC) to analyze the statistical behaviors of the symbolic dynamics. Our experiments on the benchmark datasets and the clinical signals demonstrate that SAX can always reduce the complexity while preserving the core information embedded in the original time series with significant embedding efficiency. Our proposed IEC score provide a priori to determine if SAX is adequate for specific dataset, which can be generalized to evaluate other symbolic representations. Our work provides an analytical framework with several statistical tools to analyze, evaluate and further improve the symbolic dynamics for knowledge discovery in time series.
Year
Venue
Keywords
2015
2015 12TH INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY (FSKD)
SAX, Knowledge Discovery in Time Series, Infomation Embedding Cost, Permutation Entropy, Symbolic Complexity
Field
DocType
Volume
De facto standard,Data mining,Computer science,A priori and a posteriori,Theoretical computer science,Artificial intelligence,Empirical research,The Symbolic,Statistical mechanics,Embedding,Knowledge extraction,Information embedding,Machine learning
Journal
abs/1506.02732
Citations 
PageRank 
References 
1
0.35
9
Authors
4
Name
Order
Citations
PageRank
Wei Song111315.51
zhiguang wang2103.98
Yangdong Ye311829.64
Ming Fan421.06