Dynamic Symbolization of Streaming Time Series View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2004

AUTHORS

Xiaoming Jin , Jianmin Wang , Jiaguang Sun

ABSTRACT

Symbolization of time series is an important preprocessing subroutine for many data mining tasks. However, it is usually difficult, if not impossible, to apply the traditional static symbolization approach on streaming time series, because of either the low efficiency of re-computing the typical sub-series, or the low capability of representing the up-to-date series characters. This paper presents a novel symbolization method, in which the typical sub-series are dynamically adjusted to fit the up-to-date characters of streaming time series. It works in an incremental form without scanning the whole date set. Experiments on data set from stock market justify the superiority of the proposed method over the traditional ones. More... »

PAGES

559-564

Book

TITLE

Intelligent Data Engineering and Automated Learning – IDEAL 2004

ISBN

978-3-540-22881-3
978-3-540-28651-6

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-540-28651-6_82

DOI

http://dx.doi.org/10.1007/978-3-540-28651-6_82

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1036020485


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