The rapid upsurge of numerical sources of information and the growth of storage capacities in recent years has resulted in the collection of massive time series datasets. Inspired by association rule mining and other rule discovery algorithms, several approaches have been proposed in the literature to discover temporal association rules from time series data. These methods place interpretability at the top of their priorities and aim to provide domain experts with relevant and qualitative rules. In this paper, we aim to fill the gap between temporal association rule mining and time series classification t asks to increase the interpretability of current classification methods. We propose rule transform (RT), a novel algorithm for multivariate time series classification ( MTSC) t hat generates discriminative temporal rules for the sake of classification. RT generates a new feature space that represents the support of the mined temporal rules which can easily be qualitatively interpreted by domain experts. The algorithm uses Allen’s Interval Algebra to extract the most prominent temporal rules from a given dataset. To our knowledge, this is the first effort to use shapelets as a unit for temporal rule mining studies for the purpose of classification. We evaluate our algorithm on the UEA archive of multivariate time series. Results show that RT produces accuracies superior to state-of-the-art time series classification algorithms with the additional advantage of interpretability.