A novel hybrid algorithm for discovering motifs from financial time series

Abstract

Time series motifs are pairs of individual subsequences, which are very similar to each other within the time series. In this paper, we propose an efficient and novel hybrid algorithm by taking best out of two popular algorithms namely MK algorithm and EP-C algorithm that exist in literature. We demonstrate the efficiency of our approach in terms of time elapsed and distance obtained between the subsequences through experiments conducted on financial time series datasets viz., foreign exchange rates, Gold price and Crude oil price in terms of US dollars.