Chaotic characteristic analysis of network traffic time series at different time scales
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DOI: 10.1016/j.chaos.2019.109412
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- Alberto Mozo & Bruno Ordozgoiti & Sandra Gómez-Canaval, 2018. "Forecasting short-term data center network traffic load with convolutional neural networks," PLOS ONE, Public Library of Science, vol. 13(2), pages 1-31, February.
- Zheng, Lingwei & Liu, Zhaokun & Shen, Junnan & Wu, Chenxi, 2018. "Very short-term maximum Lyapunov exponent forecasting tool for distributed photovoltaic output," Applied Energy, Elsevier, vol. 229(C), pages 1128-1139.
- Rongsheng Liu & Minfang Peng & Xianghui Xiao, 2018. "Ultra-Short-Term Wind Power Prediction Based on Multivariate Phase Space Reconstruction and Multivariate Linear Regression," Energies, MDPI, vol. 11(10), pages 1-17, October.
- Dlask, Martin & Kukal, Jaromir, 2017. "Application of rotational spectrum for correlation dimension estimation," Chaos, Solitons & Fractals, Elsevier, vol. 99(C), pages 256-262.
- Jie Ran & Yuqin Li & Changchun Wang, 2018. "Chaos and Complexity Analysis of a Discrete Permanent-Magnet Synchronous Motor System," Complexity, Hindawi, vol. 2018, pages 1-8, December.
- Jie Cao & Zhiyi Fang & Guannan Qu & Hongyu Sun & Dan Zhang, 2017. "An accurate traffic classification model based on support vector machines," International Journal of Network Management, John Wiley & Sons, vol. 27(1), January.
- Mukherjee, Somenath & Ray, Rajdeep & Samanta, Rajkumar & Khondekar, Mofazzal H. & Sanyal, Goutam, 2017. "Nonlinearity and chaos in wireless network traffic," Chaos, Solitons & Fractals, Elsevier, vol. 96(C), pages 23-29.
- Shaw, Pankaj Kumar & Chaubey, Neeraj & Mukherjee, S. & Janaki, M.S. & Iyengar, A.N. Sekar, 2019. "A continuous transition from chaotic bursting to chaotic spiking in a glow discharge plasma and its associated long range correlation to anti correlation behaviour," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 513(C), pages 126-134.
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- Tang, Li-Hong & Bai, Yu-Long & Yang, Jie & Lu, Ya-Ni, 2020. "A hybrid prediction method based on empirical mode decomposition and multiple model fusion for chaotic time series," Chaos, Solitons & Fractals, Elsevier, vol. 141(C).
- Méndez-Gordillo, Alma Rosa & Cadenas, Erasmo, 2021. "Wind speed forecasting by the extraction of the multifractal patterns of time series through the multiplicative cascade technique," Chaos, Solitons & Fractals, Elsevier, vol. 143(C).
- Zhang, Yuan & Cao, Jinde & Liu, Lixia & Liu, Haihong & Li, Zhouhong, 2024. "Complex role of time delay in dynamical coordination of neural progenitor fate decisions mediated by Notch pathway," Chaos, Solitons & Fractals, Elsevier, vol. 180(C).
- Wang, Jujie & Cui, Quan & He, Maolin, 2022. "Hybrid intelligent framework for carbon price prediction using improved variational mode decomposition and optimal extreme learning machine," Chaos, Solitons & Fractals, Elsevier, vol. 156(C).
- Liumeng Yang & Ruichun He & Jie Wang & Hongxing Zhao & Huo Chai, 2024. "Analysis of Dynamic Behavior of Gravity Model Using the Techniques of Road Saturation and Hilbert Curve Dimensionality Reduction," Sustainability, MDPI, vol. 16(13), pages 1-19, July.
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Keywords
Network traffic; Chaotic; Time scale; Fractal; Time series;All these keywords.
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