Machine Learning Methods for Classification of Chaotic Time Series

Student: Mariya Krylova

Faculty: Faculty of Informatics, Mathematics, and Computer Science (HSE Nizhny Novgorod)

Educational Programme: Mathematics (Master)

Year of Graduation: 2024

This work is dedicated to identifying the patterns of specific burst-spike bahavior in the complex dynamics of the Chialvo map using machine learning algorithms. Based on an analytical study of the behavior of the Chialvo map [1], we identified regions corresponding to discrete chaotic Shilnikov attractors. These attractors were analyzed and used as a training dataset for the predictive models. The trained model by processing a short-term time series of the map trajectory correctly classified unstable focuses when there are atypical oscillatory activity, and when there are not.

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