bandt-pompe-master.zip

In this work, we propose a technique for the extraction and selection of relevant and non-redundant multivariate ordinal patterns from the high-dimensional combinatorial search space. Our proposed approach $ordex$, simultaneously extracts and scores the relevance and redundancy of ordinal patterns without training a classifier. As a filter-based approach, ordex aims to select a set of relevant patterns with complementary information. <br> Hence, using our scoring function based on the principles of Chebyshev's inequality, we maximize the relevance of the patterns and minimize the correlation between them. Our experiments on real-world datasets show that ordinality in time series contains valuable information for classification in several applications.