تقرير
Evaluating generation of chaotic time series by convolutional generative adversarial networks
| العنوان: | Evaluating generation of chaotic time series by convolutional generative adversarial networks |
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| المؤلفون: | Tanaka, Yuki, Yamaguti, Yutaka |
| المصدر: | JSIAM Letters, 15 (2023), 117-120 |
| سنة النشر: | 2023 |
| المجموعة: | Computer Science Nonlinear Sciences |
| مصطلحات موضوعية: | Computer Science - Machine Learning, Nonlinear Sciences - Chaotic Dynamics |
| الوصف: | To understand the ability and limitations of convolutional neural networks to generate time series that mimic complex temporal signals, we trained a generative adversarial network consisting of deep convolutional networks to generate chaotic time series and used nonlinear time series analysis to evaluate the generated time series. A numerical measure of determinism and the Lyapunov exponent, a measure of trajectory instability, showed that the generated time series well reproduce the chaotic properties of the original time series. However, error distribution analyses showed that large errors appeared at a low but non-negligible rate. Such errors would not be expected if the distribution were assumed to be exponential. |
| نوع الوثيقة: | Working Paper |
| DOI: | 10.14495/jsiaml.15.117 |
| URL الوصول: | http://arxiv.org/abs/2305.16729 |
| رقم الأكسشن: | edsarx.2305.16729 |
| قاعدة البيانات: | arXiv |
| DOI: | 10.14495/jsiaml.15.117 |
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