Understanding how a classifier makes predictions is an increasingly important problem in Machine Learning. One very popular approach is to understand how much each feature in the input contributes toward the final classification. The goal of this research project is to make progress in the field of Saliency Map Explanation applied to Multivariate Time Series data, by understanding the strengths and limitations of the available explanation methods and proposing new methods to evaluate and improve them.