Discord Discovery in Streaming Time Series based on an Improved HOT SAX Algorithm

Abstract

In this paper, we propose an improved variant of HOT SAX algorithm, called HS-Squeezer, for efficient discord detection in static time series. HS-Squeezer employs clustering rather than augmented trie to arrange two ordering heuristics in HOT SAX. Furthermore, we introduce HS-Squeezer-Stream, the application of HS-Squeezer in the framework for detecting local discords in streaming time series. The experimental results reveal that HS-Squeezer can detect the same quality discords as those detected by HOT SAX but with much shorter run time. Furthermore, HS-Squeezer-Stream demonstrates a fast response in handling time series streams with quality local discords detected.