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arXiv · 1701.06446

Algorithm for an arbitrary-order cumulant tensor calculation in a sliding window of data streams

Krzysztof Domino·Piotr Gawron
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Abstract

High order cumulant tensors carry information about statistics of non-normally distributed multivariate data. In this work we present a new efficient algorithm for calculation of cumulants of arbitrary order in a sliding window for data streams. We showed that this algorithms enables speedups of cumulants updates compared to current algorithms. This algorithm can be used for processing on-line high-frequency multivariate data and can find applications in, e.g., on-line signal filtering and classification of data streams. To present an application of this algorithm, we propose an estimator of non-Gaussianity of a data stream based on the norms of high-order cumulant tensors. We show how to detect the transition from Gaussian distributed data to non-Gaussian ones in a~data stream. In order to achieve high implementation efficiency of operations on super-symmetric tensors, such as cumulant tensors, we employ the block structure to store and calculate only one hyper-pyramid part of such tensors.

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Krzysztof Domino, Piotr Gawron. 2017-01-20. Algorithm for an arbitrary-order cumulant tensor calculation in a sliding window of data streams. https://doi.org/10.2478/amcs-2019-0015

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