Time Series Discord Discovery Based on iSAX Symbolic Representation

Among several algorithms have been proposed to solve the problem of time series discord discovery, HOT SAX is one of the widely used algorithms. In this work, we employ state-of-the-art iSAX representation in time series discord discovery. We propose a new time series discord discovery algorithm, called HOTiSAX, by employing iSAX rather than SAX representation in discord discovery algorithm. The incorporation requires two new auxiliary functions to handle approximate non-self match search and exact non-self match search in the discord discovery algorithm. Besides, we devise a new heuristic to offer a better ordering for examining subsequences in the outer loop of HOTiSAX algorithm. We evaluate our algorithm with a set of experiments. Experimental results show that the new algorithm HOTiSAX outperforms the previous HOT SAX.