Textual Approximation Methods for Time Series Classification: TAX and l-TAX

Abdulla Al MARUF, Hung-Hsuan HUANG, Kyoji KAWAGOE

Summary :

A lot of work has been conducted on time series classification and similarity search over the past decades. However, the classification of a time series with high accuracy is still insufficient in applications such as ubiquitous or sensor systems. In this paper, a novel textual approximation of a time series, called TAX, is proposed to achieve high accuracy time series classification. l-TAX, an extended version of TAX that shows promising classification accuracy over TAX and other existing methods, is also proposed. We also provide a comprehensive comparison between TAX and l-TAX, and discuss the benefits of both methods. Both TAX and l-TAX transform a time series into a textual structure using existing document retrieval methods and bioinformatics algorithms. In TAX, a time series is represented as a document like structure, whereas l-TAX used a sequence of textual symbols. This paper provides a comprehensive overview of the textual approximation and techniques used by TAX and l-TAX

• Publication: IEICE TRANSACTIONS on Information Vol.E97-D No.4 pp.798-810
• Publication Date: 2014/04/01
• Online ISSN: 1745-1361
• DOI: 10.1587/transinf.E97.D.798
• Type of Manuscript: Special Section PAPER (Special Section on Data Engineering and Information Management)

Authors

Abdulla Al MARUF
  Ritsumeikan University
Hung-Hsuan HUANG
  Ritsumeikan University
Kyoji KAWAGOE
  Ritsumeikan University

Keyword

time series,  similarity search,  textual approximation,  document retrievals,  key points,  longest common subsequence