Prediction of multivariate chaotic time series with local polynomial fitting

DOI10.1016/J.CAMWA.2009.10.019zbMath1189.37090OpenAlexW2023128139MaRDI QIDQ980265

Li-Yun Su

Publication date: 28 June 2010

Published in: Computers \& Mathematics with Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.camwa.2009.10.019

zbMATH Keywords

predictionlocal polynomial fittinglocal linear prediction\(BP\) neural networks predictionlocal mean predictionmultivariate chaotic time series

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Time series analysis of dynamical systems (37M10)

Related Items (6)

A study of numerical integration based on Legendre polynomial and RLS algorithm ⋮ Two-stage method based on local polynomial fitting for a linear heteroscedastic regression model and its application in economics ⋮ Deconvolution of defocused image with multivariate local polynomial regression and iterative Wiener filtering in DWT domain ⋮ Local functional coefficient autoregressive model for multistep prediction of chaotic time series ⋮ Local polynomial regression solution for partial differential equations with initial and boundary values ⋮ Generalised latent assimilation in heterogeneous reduced spaces with machine learning surrogate models

Cites Work

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• Statistical estimation in varying coefficient models
• Predicting a chaotic time series using a fuzzy neural network
• Dynamics from multivariate time series
• Particle-filter-based estimation and prediction of chaotic states
• Modelling of chaotic time series using a variable-length windowing approach
• Independent coordinates for strange attractors from mutual information
• Prediction of Chaotic Time Series Based on the Recurrent Predictor Neural Network

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