A novel Bayesian and Chain Rule Model on symbolic representation for time series classification

Time series data are generated in abundance of information technology applications. Classification of time series is one of the significant areas of interest in Time Series Data Mining. Over the last decade, different approaches have been developed to achieve a superior effectiveness and efficiency in classification of time series. We propose a new model for classification of time series which first uses SAX (symbolic aggregate approximation) method to transform the time series to symbols and then applies a probabilistic methodology in dealing with classification of symbolic sequences. This model is developed based on Bayesian rule and Probability Chain rule on symbolic representation of time series along with considering a penalty for mismatching points. We call this model Bayesian and Chain rule Model (BCM). The performance of BCM has been evaluated using 43 time series data sets. We performed an extensive experiment to compare the performance of BCM to Euclidean Distance, Dynamic Time Warping (DTW) with best warping window, and DTW with no warping window. This comparison was done through three stages. Stage 1 was predicting the domains and problems, in which BCM works relatively well. Stage 2 included measuring the prediction accuracy of BCM to those of the other methods. Stage 3 involved constructing the confusion matrixes for predictions in Stage 1 as well as measuring and comparing the predictability of the methods. The results show that, compared to the other popular time series classification methods, BCM is more accurate where it is predicted to perform better.