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.
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.