@article{bibcite_15249,
  author = {Krzysztof Domino and Piotr Gawron},
  title = {An algorithm for arbitrary{\textendash}order cumulant tensor calculation in a sliding window of data streams},
  abstract = {<p>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. To present an application of the algorithm, we propose a measure of non-normality of data stream based on tensor 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.</p>
},
  year = {2019},
  journal = {International Journal of Applied Mathematics and Computer Science},
  volume = {29},
  chapter = {195},
  pages = {206},
  month = {2019},
  doi = {10.2478/amcs-2019-0015},
  language = {eng},
}