Discovering time series discord based on a decrete method

Abstract

A time series is a series of data points indexed in time order. Most commonly, a time series is a sequence taken at successive equally spaced points in time. Discord in a long time series is a subsequence which is the most different from all the rest of subsequences of that time series. Time series discord discovery is one of problems which has received a lot of attention lately. In this paper, we propose a new algorithm for time series discord discovery which is based on the discrete method called Symbolic Aggregate approximation (SAX) method using distance measure in SAX space and Euclidean distance associated with the idea of early abandoning. Our proposed method only need to scan the database two times to discover time series discord exactly and it is very simple to implement. The experimental results showed that our proposed method outperforms the similar method proposed by Yankov et al., in terms of runtime while the accuracy is the same.