Stock temporal prediction based on time series motifs

Recent researches pay more attention to stock tendency prediction, which various machine learning approaches have been proposed. In this paper, we propose an algorithm to discover self-correlation of stock price in virtue of the notion of time series motifs, by viewing stock price sequences as time series. Generally, time series motif is a pattern appearing frequently in a time sequence, useful to forecast the stock temporal tendencies and prices as a reliable part in time series. In the proposed approach, we firstly search for one part of time series motifs using ordinal comparison and k-NN clustering algorithm, and then attempt to discover the correlation between motifs and subsequences connected behind them. Experimental results demonstrate the positive contribution of time series motifs, the acceptable prediction accuracy, and priority of our algorithm.