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

• Published in: Proceedings (IEEE International Conference on Data Mining) pp. 1175 - 1180
• Main Authors: Senin, Pavel, Malinchik, Sergey
• Format: Conference Proceeding Journal Article
• Language: English
• Published: IEEE 01.12.2013
• Subjects: Accuracy; Aggregates; Approximation; Approximation algorithms; Approximation methods; Classification; classification algorithms; Conferences; Data mining; Euclidean distance; Learning; Mathematical analysis; Time series; Time series analysis; Training; Vector spaces; Vectors

In this paper, we propose a novel method for discovering characteristic patterns in a time series called SAX-VSM. This method is based on two existing techniques - Symbolic Aggregate approximation and Vector Space Model. SAX-VSM automatically discovers and ranks time series patterns by their "i...