A hidden semi-Markov model for chart pattern matching in financial time series

Many pattern matching approaches have been applied in financial time series to detect chart patterns and predict price trends. In this paper, we propose an extended hidden semi-Markov model for chart pattern matching (HSMM-CP). In our approach, a hidden semi-Markov model is trained and a Viterbi algorithm is used to detect chart patterns. The proposed approach not only simplifies the traditional way of training an HSMM, but also reduces potential biases in parameter initialisation. We compare the proposed model with current approaches on a set of templates selected from 53 chart patterns. Experiments on a synthetic dataset show that the proposed approach has the highest average accuracy and recall among other pattern matching approaches. Specifically, the HSMM-CP approach achieves highest accuracy for Triangles, Ascending, Head-and-Shoulders Tops, Triple Tops and Cup with Handle patterns. Moreover, experiments results show that the HSMM-CP performs significantly better than other approaches in distinguishing patterns with similar shapes such as Head-and-Shoulders Tops and Triple Tops. Experiments are also conducted on a real dataset comprising the historical prices of several stocks.