Discretization is a crucial first step in several time series mining applications. Our research proposes a novel method to discretize time series data and develops a similarity score based on the discretized representation. The similarity score allows us to compare two time series sequences and enables us to perform pattern learning tasks such as clustering, clas-sification, and anomaly detection. We propose a generative model for discretization based on multiple normal distribu-tions and create an optimization technique to learn parame-ters of these normal distributions. To show the effectiveness of our approach, we perform comprehensive experiments in classifying datasets from the UCR time series repository.