SAX-VSM: Interpretable Time Series Classification Using SAX and Vector Space Model

Abstract

In this paper, we propose a novel method for characteristic patterns discovery in time series. This method, called SAX-VSM, is based on two existing techniques - Symbolic Aggregate approXimation and Vector Space Model. SAX-VSM is capable to automatically discover and rank time series patterns by their importance to the class, which not only creates well-performing classifiers and facilitates clustering, but also provides an interpretable class generalization. The accuracy of the method, as shown through experimental evaluation, is at the level of the current state of the art. While being relatively computationally expensive within a learning phase, our method provides fast, precise, and interpretable classification.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2013
Accession Number
ADA603196

Entities

People

  • Pavel Senin
  • Sergey Malinchik

Organizations

  • Indiana University

Tags

Communities of Interest

  • Biomedical

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence
  • Computational Complexity
  • Computational Science
  • Computer Science
  • Data Mining
  • Data Sets
  • Data Visualization
  • Dictionaries
  • Health Care
  • Information Retrieval
  • Information Science
  • Machine Learning
  • Signal Processing
  • Standards
  • Training
  • Vector Spaces

Fields of Study

  • Computer science

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Computational Fluid Dynamics (CFD)
  • Regression Analysis.

Technology Areas

  • Space