Data series classification is an important and challenging problem in data science. Explaining the classification decisions by finding the discriminant parts of the input that led the algorithm to some decision is a real need in many applications. Convolutional neural networks perform well for the data series classification task; though, the explanations provided by this type of algorithms are poor for the specific case of multivariate data series. Addressing this important limitation is a significant challenge. In this paper, we propose a novel method that solves this problem by highlighting both the temporal and dimensional discriminant information. Our contribution is two-fold: we first describe a convolutional architecture that enables the comparison of dimensions; then, we propose a method that returns dCAM, a Dimension-wise Class Activation Map specifically designed for multivariate time series (and CNN-based models). Experiments with several synthetic and real datasets demonstrate that dCAM is not only more accurate than previous approaches, but the only viable solution for discriminant feature discovery and classification explanation in multivariate time series.

Subsequence anomaly detection in long sequences is an important problem with applications in a wide range of domains. However, the state-of-the-art approaches have severe limitations: they either require prior domain knowledge, or become cumbersome and inefficient/ineffective in situations with recurrent anomalies of the same type. We recently proposed Series2Graph, a novel method based on a graph representation of a low-dimensionality embedding of subsequences, that detects anomalous subsequences. The experimental results, on the largest set of synthetic and real datasets used to date, demonstrate that the proposed approach correctly identifies single and recurrent anomalies of various types without any prior knowledge of the characteristics of these anomalies, outperforming by a large margin several competing approaches in accuracy, while being up to orders of magnitude faster. In this demonstration, we present GraphAn, a system based on Series2Graph, show-case the challenges of the problem, and demonstrate the advantages of the proposed system.