2026-10-11T13:40:39Z https://api.figshare.com/v2/oai

oai:figshare.com:article/33823594 2026-09-15T21:07:08Z portal_1197 item_type_3 month_year_09_2026

Data for Stochastic approach for assessing the predictability of chaotic time-series using reservoir computing I. A. Khovanov (17196715) Chaotic behavior in systems Machine learning Neural networks (Computer science) Industry 4.0 Migrated from ePrints The applicability of machine learning for predicting chaotic dynamics relies heavily upon the data used in the training stage. Chaotic time series obtained by numerically solving ordinary differential equations embed a complicated noise of the applied numerical scheme. Such a dependence of the solution on the numeric scheme leads to an inadequate representation of the real chaotic system. A stochastic approach for generating training time series and characterizing their predictability is suggested to address this problem. The approach is applied for analyzing two chaotic systems with known properties, the Lorenz system and the Anishchenko–Astakhov generator. Additionally, the approach is extended to critically assess a reservoir computing model used for chaotic time series prediction. Limitations of reservoir computing for surrogate modeling of chaotic systems are highlighted.<br><br>Dataset consists of a set of folders for figures shown in the paper. Data are included in ASCII format. Each folder contains file "comment.txt" with relevant to each figure information. File 'comment_on_numerical_methods.pdf' is a brief description of the used numerical methods. 2021-07-20T00:00:00Z Dataset Dataset 10.82444/warw.33823594.v1 https://figshare.com/articles/dataset/Data_for_Stochastic_approach_for_assessing_the_predictability_of_chaotic_time-series_using_reservoir_computing/33823594 CC BY 4.0