Accurate symbolization of time series

Authors:
Xinqiang Zuo;Xiaoming Jin
Affiliations:
School of Software, Tsinghua University, Beijing, China;School of Software, Tsinghua University, Beijing, China
Venue:
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Year:
2005

Citing 4
Cited 0

Distribution Discovery: Local Analysis of Temporal Rules

PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
A symbolic representation of time series, with implications for streaming algorithms

DMKD '03 Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
Clustering of Time Series Subsequences is Meaningless: Implications for Previous and Future Research

ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Using triangle inequality to efficiently process continuous queries on high-dimensional streaming time series

SSDBM '03 Proceedings of the 15th International Conference on Scientific and Statistical Database Management

Quantified Score

Hi-index	0.00

Visualization

Abstract

Symbolization is a useful method for mining time series. As our experimental results demonstrated, the previous methods are not accurate enough due to their limitations in handling a prevalent kind of time series in which similar movements are often with different lengths. This paper considers the accuracy issue of symbolization of time series. We propose a novel approach that emphasizes the meaning of each movement in the time series, regardless of the length or shift of it. To make the proposed approach more practicable, we also provide a semiautomatic method for setting the parameters. The nature of the problem and the performance of our approach had been analyzed on both real data and synthetic data. Experimental results justified the superiority of our approach over the previous one and gave some useful empirical conclusions.