Interval Type-2 Fuzzy Neural Networks for Chaotic Time Series Prediction: A Concise Overview
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Transactions on Cybernetics"
DOI: 10.1109/tcyb.2018.2834356
Abstract: Chaotic time series widely exists in nature and society (e.g., meteorology, physics, economics, etc.), which usually exhibits seemingly unpredictable features due to its inherent nonstationary and high complexity. Thankfully, multifarious advanced approaches have been developed… read more here.
Keywords: series prediction; prediction; chaotic time; time series ... See more keywords
Learning Both Dynamic-Shared and Dynamic-Specific Patterns for Chaotic Time-Series Prediction.
Sign Up to like & getrecommendations! Published in 2020 at "IEEE transactions on cybernetics"
DOI: 10.1109/tcyb.2020.3017736
Abstract: In the real world, multivariate time series from the dynamical system are correlated with deterministic relationships. Analyzing them dividedly instead of utilizing the shared-pattern of the dynamical system is time consuming and cumbersome. Multitask learning… read more here.
Keywords: time; time series; dynamic shared; prediction ... See more keywords
Nonlinear Spiking Neural Systems With Autapses for Predicting Chaotic Time Series.
Sign Up to like & getrecommendations! Published in 2023 at "IEEE transactions on cybernetics"
DOI: 10.1109/tcyb.2023.3270873
Abstract: Spiking neural P (SNP) systems are a class of distributed and parallel neural-like computing models that are inspired by the mechanism of spiking neurons and are 3rd-generation neural networks. Chaotic time series forecasting is one… read more here.
Keywords: chaotic time; spiking neural; systems autapses; time series ... See more keywords
Numerical and experimental investigation of a dispersive optoelectronic oscillator for chaotic time-delay signature suppression.
Sign Up to like & getrecommendations! Published in 2023 at "Optics express"
DOI: 10.1364/oe.484659
Abstract: Chaos generation from a novel single-loop dispersive optoelectronic oscillator (OEO) with a broadband chirped fiber Bragg grating (CFBG) is numerically and experimentally investigated. The CFBG has much broader bandwidth than the chaotic dynamics such that… read more here.
Keywords: chaotic time; time delay; delay signature; dispersive optoelectronic ... See more keywords
Chaotic time series prediction using wavelet transform and multi-model hybrid method
Sign Up to like & getrecommendations! Published in 2019 at "Journal of Vibroengineering"
DOI: 10.21595/jve.2019.20579
Abstract: In order to further improve the prediction accuracy of the chaotic time series and overcome the defects of the single model, a multi-model hybrid model of chaotic time series is proposed. First, the Discrete Wavelet… read more here.
Keywords: model; prediction; time series; chaotic time ... See more keywords
A Hybrid Method Using HAVOK Analysis and Machine Learning for Predicting Chaotic Time Series
Sign Up to like & getrecommendations! Published in 2022 at "Entropy"
DOI: 10.3390/e24030408
Abstract: The prediction of chaotic time series systems has remained a challenging problem in recent decades. A hybrid method using Hankel Alternative View Of Koopman (HAVOK) analysis and machine learning (HAVOK-ML) is developed to predict chaotic… read more here.
Keywords: time; time series; havok analysis; machine learning ... See more keywords
Chaotic Time Series Forecasting Approaches Using Machine Learning Techniques: A Review
Sign Up to like & getrecommendations! Published in 2022 at "Symmetry"
DOI: 10.3390/sym14050955
Abstract: Traditional statistical, physical, and correlation models for chaotic time series prediction have problems, such as low forecasting accuracy, computational time, and difficulty determining the neural network’s topologies. Over a decade, various researchers have been working… read more here.
Keywords: chaotic time; neural network; time; series forecasting ... See more keywords